Repository: DIYgod/RSSHub
Pull request #18731: fix(route): optimize transformer-circuits route
Author: login: shinmohuang
Labels: name: auto: ready to review
description: Manual review will come in after lint issues and merge conflicts are fixed
name: route
Created: 2025-03-29T10:51:20Z | Merged: 2025-03-30T15:06:10Z

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## Involved Issue / 该 PR 相关 Issue

Close #18729

## Example for the Proposed Route(s) / 路由地址示例

```routes
/transformer-circuits
```


## New RSS Route Checklist / 新 RSS 路由检查表
  
- [ ] New Route / 新的路由
  - [x] Follows [Script Standard](https://docs.rsshub.app/joinus/advanced/script-standard) / 跟随 [路由规范](https://docs.rsshub.app/zh/joinus/advanced/script-standard)
- [ ] Anti-bot or rate limit / 反爬/频率限制
  - [ ] If yes, do your code reflect this sign? / 如果有, 是否有对应的措施?
- [x] [Date and time](https://docs.rsshub.app/joinus/advanced/pub-date) / [日期和时间](https://docs.rsshub.app/zh/joinus/advanced/pub-date)
  - [x] Parsed / 可以解析
  - [x] Correct time zone / 时区正确
- [ ] New package added / 添加了新的包
- [ ] `Puppeteer`

## Note / 说明
Optimize transformer-circuits route, can fetch full text now


Diff:
diff --git a/lib/routes/transformer-circuits/index.ts b/lib/routes/transformer-circuits/index.ts
index 8ecd33b4ed9e..ef5d2aa860e0 100644
--- a/lib/routes/transformer-circuits/index.ts
+++ b/lib/routes/transformer-circuits/index.ts
@@ -1,7 +1,16 @@
 import { Route, DataItem } from '@/types';
+import cache from '@/utils/cache';
 import ofetch from '@/utils/ofetch';
 import { load } from 'cheerio';
 import { parseDate } from '@/utils/parse-date';
+import { art } from '@/utils/render';
+import path from 'node:path';
+import { fileURLToPath } from 'node:url';
+import logger from '@/utils/logger';
+
+// 为ES模块创建__dirname等价物
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = path.dirname(__filename);
 
 // Define the main route path
 export const route: Route = {
@@ -27,8 +36,8 @@ async function handler() {
     const response = await ofetch(rootUrl);
     const $ = load(response);
 
-    // Get all the articles using .map() instead of .push()
-    const articles: DataItem[] = $('.toc a')
+    // Get all article links and basic info
+    const articlePromises = $('.toc a')
         .toArray()
         .map((item) => {
             const $item = $(item);
@@ -40,40 +49,76 @@ async function handler() {
             if (currentElement.hasClass('paper') || currentElement.hasClass('note')) {
                 const articleType = currentElement.hasClass('paper') ? 'Paper' : 'Note';
 
-                // Extract title
+                // Extract title and metadata from the list page
                 const title = currentElement.find('h3').text().trim();
-
-                // Extract author if available (papers have bylines, notes don't always have them)
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
-                // Extract description
                 const description = currentElement.find('.description').text().trim();
-
-                // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
-                return {
-                    title,
-                    link: articleUrl,
-                    pubDate: parseDate(currentDate, 'MMMM YYYY'),
-                    author,
-                    description: `${articleType}: ${description}`,
-                    category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
-                };
+                // Cache the whole article object instead of just the content
+                return cache.tryGet(articleUrl, async () => {
+                    const fullContent = await fetchArticleContent(articleUrl);
+                    return {
+                        title,
+                        link: articleUrl,
+                        pubDate: parseDate(currentDate, 'MMMM YYYY'),
+                        author,
+                        description: fullContent || `${articleType}: ${description}`,
+                        category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
+                    };
+                });
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
+        item: articlesWithContent,
         description: 'Research on reverse engineering transformer language models into human-understandable programs',
     };
 }
+
+// Function to fetch and parse article content
+async function fetchArticleContent(url) {
+    try {
+        const response = await ofetch(url);
+        const $ = load(response);
+
+        // Remove navigation and other unnecessary elements
+        $('.article-header, .tooltip, modal, script, style, d-front-matter, .visual-toc').remove();
+
+        // Get the main content - d-article is the custom tag used by this site
+        let content = $('d-article').html();
+
+        // If d-article not found, try more specific fallback selectors
+        if (!content) {
+            // Try to find content in common article containers, but avoid selecting the entire body
+            content = $('main article, .article-content, .post-content, .content-area').html() || $('.content, .article, .post').html() || $('main').html();
+
+            // If still no content found, log a warning
+            if (!content) {
+                logger.warn(`No suitable content container found for ${url}`);
+                content = `<p>Could not extract content. Please visit <a href="${url}">the original page</a>.</p>`;
+            }
+        }
+
+        // Create an HTML fragment (not a full document) for the RSS description
+        return art(path.join(__dirname, 'templates/article.art'), {
+            content,
+            link: url,
+        });
+    } catch (error: unknown) {
+        const errorMessage = error instanceof Error ? error.message : String(error);
+        logger.error(`Error fetching article content from ${url}: ${errorMessage}`);
+        return null; // Return null on error, we'll fall back to description
+    }
+}
diff --git a/lib/routes/transformer-circuits/templates/article.art b/lib/routes/transformer-circuits/templates/article.art
new file mode 100644
index 000000000000..80de0e0b3480
--- /dev/null
+++ b/lib/routes/transformer-circuits/templates/article.art
@@ -0,0 +1,62 @@
+<style>
+    .content-wrapper {
+        font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+        line-height: 1.6;
+        color: #333;
+    }
+    img {
+        max-width: 100%;
+        height: auto;
+    }
+    pre, code {
+        background-color: #f5f5f5;
+        border-radius: 3px;
+        padding: 0.2em 0.4em;
+        overflow-x: auto;
+    }
+    a {
+        color: #0366d6;
+        text-decoration: none;
+    }
+    a:hover {
+        text-decoration: underline;
+    }
+    h1, h2, h3, h4, h5, h6 {
+        margin-top: 24px;
+        margin-bottom: 16px;
+        font-weight: 600;
+        line-height: 1.25;
+    }
+    p, ul, ol {
+        margin-bottom: 16px;
+    }
+    .read-original {
+        margin-top: 30px;
+        margin-bottom: 30px;
+        text-align: center;
+        padding: 10px;
+        background-color: #f7f7f7;
+        border-radius: 4px;
+    }
+    /* Support for custom elements used on transformer-circuits website */
+    d-figure, figure {
+        margin: 20px 0;
+        text-align: center;
+    }
+    d-byline {
+        font-size: 0.9em;
+        color: #666;
+        margin: 15px 0;
+    }
+    .gdoc-image img {
+        max-width: 100%;
+        display: block;
+        margin: 0 auto;
+    }
+</style>
+<div class="content-wrapper">
+    {{@ content }}
+</div>
+<div class="read-original">
+    <a href="{{ link }}" target="_blank">Read Original</a>
+</div> 
\ No newline at end of file


Reviews:
user: login: TonyRL
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-03-29T12:37:28Z
user: login: TonyRL
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-03-29T13:59:19Z
user: login: TonyRL
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-03-30T06:28:54Z
user: login: TonyRL
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-03-30T06:32:18Z
user: login: shinmohuang
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-30T13:59:04Z
user: login: TonyRL
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-03-30T14:59:07Z

Review comments:
diff_hunk: @@ -43,37 +47,81 @@ async function handler() {
                 // Extract title
                 const title = currentElement.find('h3').text().trim();
 
-                // Extract author if available (papers have bylines, notes don't always have them)
+                // Extract author if available
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
+
                 // Extract description
                 const description = currentElement.find('.description').text().trim();
 
                 // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
+                // 使用 cache.tryGet 缓存文章内容
+                const fullContent = await cache.tryGet(
+                    articleUrl,
+                    async () => await fetchArticleContent(articleUrl),
+                    60 * 60 * 24 // 缓存24小时
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: Do not hard code the cache duration. It should be managed by https://github.com/DIYgod/RSSHub/blob/5b58a1911e0644c4c0130a795d1ed9c2d82678d6/lib/config.ts#L465
created_at: 2025-03-29T12:34:19Z
author_association: COLLABORATOR
diff_hunk: @@ -43,37 +47,81 @@ async function handler() {
                 // Extract title
                 const title = currentElement.find('h3').text().trim();
 
-                // Extract author if available (papers have bylines, notes don't always have them)
+                // Extract author if available
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
+
                 // Extract description
                 const description = currentElement.find('.description').text().trim();
 
                 // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
+                // 使用 cache.tryGet 缓存文章内容
+                const fullContent = await cache.tryGet(
+                    articleUrl,
+                    async () => await fetchArticleContent(articleUrl),
+                    60 * 60 * 24 // 缓存24小时
+                );
+
+                // Create article object with full content
                 return {
                     title,
                     link: articleUrl,
                     pubDate: parseDate(currentDate, 'MMMM YYYY'),
                     author,
-                    description: `${articleType}: ${description}`,
+                    description: fullContent || `${articleType}: ${description}`,
                     category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
                 };
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
-        description: 'Research on reverse engineering transformer language models into human-understandable programs',
+        item: articlesWithContent,
+        description: 'Research on reverse engineering transformer language models into human-understandable programs, feedId:128744138938804224+userId:94101804721377280',
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: ```suggestion
        description: 'Research on reverse engineering transformer language models into human-understandable programs',
```
created_at: 2025-03-29T12:34:30Z
author_association: COLLABORATOR
diff_hunk: @@ -43,37 +47,81 @@ async function handler() {
                 // Extract title
                 const title = currentElement.find('h3').text().trim();
 
-                // Extract author if available (papers have bylines, notes don't always have them)
+                // Extract author if available
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
+
                 // Extract description
                 const description = currentElement.find('.description').text().trim();
 
                 // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
+                // 使用 cache.tryGet 缓存文章内容
+                const fullContent = await cache.tryGet(
+                    articleUrl,
+                    async () => await fetchArticleContent(articleUrl),
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: Cache the whole returned object https://github.com/DIYgod/RSSHub/blob/1a87938ec3dd1efd7f8ace94722a004de606f4a7/lib/routes/transformer-circuits/index.ts#L72-L79
created_at: 2025-03-29T12:35:23Z
author_association: COLLABORATOR
diff_hunk: @@ -43,37 +47,81 @@ async function handler() {
                 // Extract title
                 const title = currentElement.find('h3').text().trim();
 
-                // Extract author if available (papers have bylines, notes don't always have them)
+                // Extract author if available
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
+
                 // Extract description
                 const description = currentElement.find('.description').text().trim();
 
                 // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
+                // 使用 cache.tryGet 缓存文章内容
+                const fullContent = await cache.tryGet(
+                    articleUrl,
+                    async () => await fetchArticleContent(articleUrl),
+                    60 * 60 * 24 // 缓存24小时
+                );
+
+                // Create article object with full content
                 return {
                     title,
                     link: articleUrl,
                     pubDate: parseDate(currentDate, 'MMMM YYYY'),
                     author,
-                    description: `${articleType}: ${description}`,
+                    description: fullContent || `${articleType}: ${description}`,
                     category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
                 };
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
-        description: 'Research on reverse engineering transformer language models into human-understandable programs',
+        item: articlesWithContent,
+        description: 'Research on reverse engineering transformer language models into human-understandable programs, feedId:128744138938804224+userId:94101804721377280',
     };
 }
+
+// Function to fetch and parse article content
+async function fetchArticleContent(url) {
+    try {
+        logger.debug(`Fetching article content: ${url}`);
+        const response = await ofetch(url, {
+            timeout: 10000,
+            retry: 2,
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: Do not hard code these values. They should be managed by https://github.com/DIYgod/RSSHub/blob/5b58a1911e0644c4c0130a795d1ed9c2d82678d6/lib/config.ts#L455-L456
created_at: 2025-03-29T12:36:19Z
author_association: COLLABORATOR
diff_hunk: @@ -43,37 +47,81 @@ async function handler() {
                 // Extract title
                 const title = currentElement.find('h3').text().trim();
 
-                // Extract author if available (papers have bylines, notes don't always have them)
+                // Extract author if available
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
+
                 // Extract description
                 const description = currentElement.find('.description').text().trim();
 
                 // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
+                // 使用 cache.tryGet 缓存文章内容
+                const fullContent = await cache.tryGet(
+                    articleUrl,
+                    async () => await fetchArticleContent(articleUrl),
+                    60 * 60 * 24 // 缓存24小时
+                );
+
+                // Create article object with full content
                 return {
                     title,
                     link: articleUrl,
                     pubDate: parseDate(currentDate, 'MMMM YYYY'),
                     author,
-                    description: `${articleType}: ${description}`,
+                    description: fullContent || `${articleType}: ${description}`,
                     category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
                 };
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
-        description: 'Research on reverse engineering transformer language models into human-understandable programs',
+        item: articlesWithContent,
+        description: 'Research on reverse engineering transformer language models into human-understandable programs, feedId:128744138938804224+userId:94101804721377280',
     };
 }
+
+// Function to fetch and parse article content
+async function fetchArticleContent(url) {
+    try {
+        logger.debug(`Fetching article content: ${url}`);
+        const response = await ofetch(url, {
+            timeout: 10000,
+            retry: 2,
+        });
+        const $ = load(response);
+
+        // Remove navigation and other unnecessary elements
+        $('.article-header, .tooltip, modal, script, style, d-front-matter, .visual-toc').remove();
+
+        // Get the main content - d-article is the custom tag used by this site
+        let content = $('d-article').html();
+
+        // If d-article not found, try fallback selectors
+        if (!content) {
+            content = $('main, article, .content, .post-content').html() || $('body').html();
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: Do not select the whole html body
created_at: 2025-03-29T12:36:48Z
author_association: COLLABORATOR
diff_hunk: @@ -40,40 +44,78 @@ async function handler() {
             if (currentElement.hasClass('paper') || currentElement.hasClass('note')) {
                 const articleType = currentElement.hasClass('paper') ? 'Paper' : 'Note';
 
-                // Extract title
+                // Extract title and metadata from the list page
                 const title = currentElement.find('h3').text().trim();
-
-                // Extract author if available (papers have bylines, notes don't always have them)
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
-                // Extract description
                 const description = currentElement.find('.description').text().trim();
-
-                // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
-                return {
-                    title,
-                    link: articleUrl,
-                    pubDate: parseDate(currentDate, 'MMMM YYYY'),
-                    author,
-                    description: `${articleType}: ${description}`,
-                    category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
-                };
+                // Cache the whole article object instead of just the content
+                return cache.tryGet(articleUrl, async () => {
+                    const fullContent = await fetchArticleContent(articleUrl);
+                    return {
+                        title,
+                        link: articleUrl,
+                        pubDate: parseDate(currentDate, 'MMMM YYYY'),
+                        author,
+                        description: fullContent || `${articleType}: ${description}`,
+                        category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
+                    };
+                });
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
+        item: articlesWithContent,
         description: 'Research on reverse engineering transformer language models into human-understandable programs',
     };
 }
+
+// Function to fetch and parse article content
+async function fetchArticleContent(url) {
+    try {
+        logger.debug(`Fetching article content: ${url}`);
+        const response = await ofetch(url);
+        const $ = load(response);
+
+        // Remove navigation and other unnecessary elements
+        $('.article-header, .tooltip, modal, script, style, d-front-matter, .visual-toc').remove();
+
+        // Get the main content - d-article is the custom tag used by this site
+        let content = $('d-article').html();
+
+        // If d-article not found, try more specific fallback selectors
+        if (!content) {
+            // Try to find content in common article containers, but avoid selecting the entire body
+            content = $('main article, .article-content, .post-content, .content-area').html() || $('.content, .article, .post').html() || $('main').html();
+
+            // If still no content found, log a warning
+            if (!content) {
+                logger.warn(`No suitable content container found for ${url}`);
+                content = `<p>Could not extract content. Please visit <a href="${url}">the original page</a>.</p>`;
+            }
+        }
+
+        // Create full HTML with proper styling
+        return art(path.join(__dirname, 'templates/article.art'), {
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: `description` in RSS a segment of HTML code, not a full-blown HTML document. It must not have DOCTYPE declaration and HTML elements like `head` `body`
created_at: 2025-03-29T13:59:19Z
author_association: COLLABORATOR
diff_hunk: @@ -40,40 +44,77 @@ async function handler() {
             if (currentElement.hasClass('paper') || currentElement.hasClass('note')) {
                 const articleType = currentElement.hasClass('paper') ? 'Paper' : 'Note';
 
-                // Extract title
+                // Extract title and metadata from the list page
                 const title = currentElement.find('h3').text().trim();
-
-                // Extract author if available (papers have bylines, notes don't always have them)
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
-                // Extract description
                 const description = currentElement.find('.description').text().trim();
-
-                // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
-                return {
-                    title,
-                    link: articleUrl,
-                    pubDate: parseDate(currentDate, 'MMMM YYYY'),
-                    author,
-                    description: `${articleType}: ${description}`,
-                    category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
-                };
+                // Cache the whole article object instead of just the content
+                return cache.tryGet(articleUrl, async () => {
+                    const fullContent = await fetchArticleContent(articleUrl);
+                    return {
+                        title,
+                        link: articleUrl,
+                        pubDate: parseDate(currentDate, 'MMMM YYYY'),
+                        author,
+                        description: fullContent || `${articleType}: ${description}`,
+                        category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
+                    };
+                });
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
+        item: articlesWithContent,
         description: 'Research on reverse engineering transformer language models into human-understandable programs',
     };
 }
+
+// Function to fetch and parse article content
+async function fetchArticleContent(url) {
+    try {
+        logger.debug(`Fetching article content: ${url}`);
+        const response = await ofetch(url);
+        const $ = load(response);
+
+        // Remove navigation and other unnecessary elements
+        $('.article-header, .tooltip, modal, script, style, d-front-matter, .visual-toc').remove();
+
+        // Get the main content - d-article is the custom tag used by this site
+        let content = $('d-article').html();
+
+        // If d-article not found, try more specific fallback selectors
+        if (!content) {
+            // Try to find content in common article containers, but avoid selecting the entire body
+            content = $('main article, .article-content, .post-content, .content-area').html() || $('.content, .article, .post').html() || $('main').html();
+
+            // If still no content found, log a warning
+            if (!content) {
+                logger.warn(`No suitable content container found for ${url}`);
+                content = `<p>Could not extract content. Please visit <a href="${url}">the original page</a>.</p>`;
+            }
+        }
+
+        // Create an HTML fragment (not a full document) for the RSS description
+        return art(path.join(__dirname, 'templates/article.art'), {
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: https://github.com/DIYgod/RSSHub/actions/runs/14146429273/job/39634285603#step:13:87
created_at: 2025-03-30T06:28:53Z
author_association: COLLABORATOR
diff_hunk: @@ -40,40 +44,77 @@ async function handler() {
             if (currentElement.hasClass('paper') || currentElement.hasClass('note')) {
                 const articleType = currentElement.hasClass('paper') ? 'Paper' : 'Note';
 
-                // Extract title
+                // Extract title and metadata from the list page
                 const title = currentElement.find('h3').text().trim();
-
-                // Extract author if available (papers have bylines, notes don't always have them)
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
-                // Extract description
                 const description = currentElement.find('.description').text().trim();
-
-                // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
-                return {
-                    title,
-                    link: articleUrl,
-                    pubDate: parseDate(currentDate, 'MMMM YYYY'),
-                    author,
-                    description: `${articleType}: ${description}`,
-                    category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
-                };
+                // Cache the whole article object instead of just the content
+                return cache.tryGet(articleUrl, async () => {
+                    const fullContent = await fetchArticleContent(articleUrl);
+                    return {
+                        title,
+                        link: articleUrl,
+                        pubDate: parseDate(currentDate, 'MMMM YYYY'),
+                        author,
+                        description: fullContent || `${articleType}: ${description}`,
+                        category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
+                    };
+                });
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
+        item: articlesWithContent,
         description: 'Research on reverse engineering transformer language models into human-understandable programs',
     };
 }
+
+// Function to fetch and parse article content
+async function fetchArticleContent(url) {
+    try {
+        logger.debug(`Fetching article content: ${url}`);
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: RSSHub does log outgoing requests.
https://github.com/DIYgod/RSSHub/blob/65a7d502850626598d785f58bf6e554b68124c8c/lib/utils/request-rewriter/fetch.ts#L32

This cause a duplicate https://github.com/DIYgod/RSSHub/actions/runs/14146429273/job/39634285603#step:13:19
created_at: 2025-03-30T06:32:17Z
author_association: COLLABORATOR
diff_hunk: @@ -40,40 +44,77 @@ async function handler() {
             if (currentElement.hasClass('paper') || currentElement.hasClass('note')) {
                 const articleType = currentElement.hasClass('paper') ? 'Paper' : 'Note';
 
-                // Extract title
+                // Extract title and metadata from the list page
                 const title = currentElement.find('h3').text().trim();
-
-                // Extract author if available (papers have bylines, notes don't always have them)
                 let author = '';
                 const byline = currentElement.find('.byline');
                 if (byline.length) {
                     author = byline.text().trim();
                 }
-                // Extract description
                 const description = currentElement.find('.description').text().trim();
-
-                // Get the article URL
                 const href = currentElement.attr('href');
                 const articleUrl = href ? (href.startsWith('http') ? href : `${rootUrl}/${href}`) : rootUrl;
 
-                // Create article object
-                return {
-                    title,
-                    link: articleUrl,
-                    pubDate: parseDate(currentDate, 'MMMM YYYY'),
-                    author,
-                    description: `${articleType}: ${description}`,
-                    category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
-                };
+                // Cache the whole article object instead of just the content
+                return cache.tryGet(articleUrl, async () => {
+                    const fullContent = await fetchArticleContent(articleUrl);
+                    return {
+                        title,
+                        link: articleUrl,
+                        pubDate: parseDate(currentDate, 'MMMM YYYY'),
+                        author,
+                        description: fullContent || `${articleType}: ${description}`,
+                        category: ['AI', 'Machine Learning', 'Anthropic', 'Transformer Circuits'],
+                    };
+                });
             }
             return null;
-        })
-        .filter(Boolean) as DataItem[];
+        });
+
+    // Wait for all article fetches to complete
+    const articlesWithContent = (await Promise.all(articlePromises)).filter(Boolean) as DataItem[];
 
     return {
         title: 'Transformer Circuits Thread',
         link: rootUrl,
-        item: articles,
+        item: articlesWithContent,
         description: 'Research on reverse engineering transformer language models into human-understandable programs',
     };
 }
+
+// Function to fetch and parse article content
+async function fetchArticleContent(url) {
+    try {
+        logger.debug(`Fetching article content: ${url}`);
path: lib/routes/transformer-circuits/index.ts
user: login: shinmohuang
body: removed
created_at: 2025-03-30T13:59:04Z
author_association: CONTRIBUTOR
diff_hunk: @@ -1,7 +1,16 @@
 import { Route, DataItem } from '@/types';
+import cache from '@/utils/cache';
 import ofetch from '@/utils/ofetch';
 import { load } from 'cheerio';
 import { parseDate } from '@/utils/parse-date';
+import { art } from '@/utils/render';
+import path from 'path';
+import { fileURLToPath } from 'url';
path: lib/routes/transformer-circuits/index.ts
user: login: TonyRL
body: ```suggestion
import path from 'node:path';
import { fileURLToPath } from 'node:url';
```
created_at: 2025-03-30T14:59:07Z
author_association: COLLABORATOR

PR comments:
user: login: github-actions[bot]
created_at: 2025-03-29T10:56:29Z
body: Successfully [generated](https://github.com/DIYgod/RSSHub/actions/runs/14144942811) as following:

<details>
<summary><a href="http://localhost:1200/transformer-circuits">http://localhost:1200/transformer-circuits</a> - Success ✔️</summary>

```rss
<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0">
  <channel>
    <title>Transformer Circuits Thread</title>
    <link>https://transformer-circuits.pub</link>
    <atom:link href="http://localhost:1200/transformer-circuits" rel="self" type="application/rss+xml"></atom:link>
    <description>Research on reverse engineering transformer language models into human-understandable programs, feedId:128744138938804224+userId:94101804721377280 - Powered by RSSHub</description>
    <generator>RSSHub</generator>
    <webMaster>contact@rsshub.app (RSSHub)</webMaster>
    <language>en</language>
    <lastBuildDate>Sat, 29 Mar 2025 10:56:28 GMT</lastBuildDate>
    <ttl>5</ttl>
    <item>
      <title>On the Biology of a Large Language Model</title>
      <description>Paper: We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic&#39;s lightweight production model — in a variety of contexts.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/biology.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/attribution-graphs/biology.html</guid>
      <pubDate>Fri, 28 Feb 2025 16:00:00 GMT</pubDate>
      <author>Lindsey et al., 2025</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuit Tracing: Revealing Computational Graphs in Language Models</title>
      <description>Paper: We describe an approach to tracing the &quot;step-by-step&quot; computation involved when a model responds to a single prompt.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/methods.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/attribution-graphs/methods.html</guid>
      <pubDate>Fri, 28 Feb 2025 16:00:00 GMT</pubDate>
      <author>Ameisen et al., 2025</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Insights on Crosscoder Model Diffing</title>
      <description>Note: A preliminary note on using crosscoders to diff models.</description>
      <link>https://transformer-circuits.pub/2025/crosscoder-diffing-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/crosscoder-diffing-update/index.html</guid>
      <pubDate>Fri, 31 Jan 2025 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — January 2025</title>
      <description>Note: A collection of small updates: dictionary learning optimization techniques.</description>
      <link>https://transformer-circuits.pub/2025/january-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/january-update/index.html</guid>
      <pubDate>Tue, 31 Dec 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Stage-Wise Model Diffing</title>
      <description>Note: A preliminary note on model diffing through dictionary fine-tuning.</description>
      <link>https://transformer-circuits.pub/2024/model-diffing/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/model-diffing/index.html</guid>
      <pubDate>Sat, 30 Nov 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Sparse Crosscoders for Cross-Layer Features and Model Diffing</title>
      <description>Note: A preliminary note on a way to get consistent features across layers, and even models.</description>
      <link>https://transformer-circuits.pub/2024/crosscoders/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/crosscoders/index.html</guid>
      <pubDate>Mon, 30 Sep 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Using Dictionary Learning Features as Classifiers</title>
      <description>Note: A preliminary note comparing feature-based and raw-activation based harmfulness classifiers.</description>
      <link>https://transformer-circuits.pub/2024/features-as-classifiers/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/features-as-classifiers/index.html</guid>
      <pubDate>Mon, 30 Sep 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — September 2024</title>
      <description>Note: A collection of small updates: investigating successor heads, oversampling data in SAEs.</description>
      <link>https://transformer-circuits.pub/2024/september-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/september-update/index.html</guid>
      <pubDate>Sat, 31 Aug 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — August 2024</title>
      <description>Note: A collection of small updates: interpretability evals, reproducing self-explanation.</description>
      <link>https://transformer-circuits.pub/2024/august-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/august-update/index.html</guid>
      <pubDate>Wed, 31 Jul 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2024</title>
      <description>Note: A collection of small updates: five hurdles, linear representations, dark matter, pivot tables, feature sensitivity.</description>
      <link>https://transformer-circuits.pub/2024/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/july-update/index.html</guid>
      <pubDate>Sun, 30 Jun 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — June 2024</title>
      <description>Note: A collection of small updates: topk and gated SAE investigation.</description>
      <link>https://transformer-circuits.pub/2024/june-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/june-update/index.html</guid>
      <pubDate>Fri, 31 May 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from Claude 3 Sonnet. Some appear to be safety-relevant.</description>
      <link>https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</guid>
      <pubDate>Tue, 30 Apr 2024 16:00:00 GMT</pubDate>
      <author>Templeton et al., 2024</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — April 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/april-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/april-update/index.html</guid>
      <pubDate>Sun, 31 Mar 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — March 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/march-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/march-update/index.html</guid>
      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Reflections on Qualitative Research</title>
      <description>Note: Some opinionated thoughts on why interpretability research may have qualitative aspects be more central than we&#39;re used to in other fields.</description>
      <link>https://transformer-circuits.pub/2024/qualitative-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/qualitative-essay/index.html</guid>
      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — February 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/feb-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/feb-update/index.html</guid>
      <pubDate>Wed, 31 Jan 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — January 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/jan-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/jan-update/index.html</guid>
      <pubDate>Sun, 31 Dec 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Towards Monosemanticity: Decomposing Language Models With Dictionary Learning</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer.</description>
      <link>https://transformer-circuits.pub/2023/monosemantic-features/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/monosemantic-features/index.html</guid>
      <pubDate>Sat, 30 Sep 2023 16:00:00 GMT</pubDate>
      <author>Bricken et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/july-update/index.html</guid>
      <pubDate>Fri, 30 Jun 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — May 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/may-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/may-update/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Interpretability Dreams</title>
      <description>Note: Our present research aims to create a foundation for mechanistic interpretability research. In doing so, it&#39;s important to keep sight of what we&#39;re trying to lay the foundations for.</description>
      <link>https://transformer-circuits.pub/2023/interpretability-dreams/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/interpretability-dreams/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Distributed Representations: Composition &amp; Superposition</title>
      <description>Note: An informal note on how &quot;distributed representations&quot; might be understood as two different, competing strategies — &quot;composition&quot; and &quot;superposition&quot; — with quite different properties.</description>
      <link>https://transformer-circuits.pub/2023/superposition-composition/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/superposition-composition/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Privileged Bases in the Transformer Residual Stream</title>
      <description>Paper: Our mathematical theories of the Transformer architecture suggest that
        individual coordinates in the residual stream should have no special
        significance, but recent work has shown that this observation is false in practice.
        We investigate this phenomenon and provisionally conclude that the per-dimension normalizers in the Adam optimizer are to blame for the effect.</description>
      <link>https://transformer-circuits.pub/2023/privileged-basis/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/privileged-basis/index.html</guid>
      <pubDate>Tue, 28 Feb 2023 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Superposition, Memorization, and Double Descent</title>
      <description>Paper: We have little mechanistic understanding of how deep learning
        models overfit to their training data, despite it being a
        central problem. Here we extend our previous work on toy models
        to shed light on how models generalize beyond their training
        data.</description>
      <link>https://transformer-circuits.pub/2023/toy-double-descent/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/toy-double-descent/index.html</guid>
      <pubDate>Sat, 31 Dec 2022 16:00:00 GMT</pubDate>
      <author>Henighan et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Toy Models of Superposition</title>
      <description>Paper: Neural networks often seem to pack many unrelated concepts into a single neuron - a puzzling phenomenon known as &#39;polysemanticity&#39;. In our latest interpretability work, we build toy models where the origins and dynamics of polysemanticity can be fully understood.</description>
      <link>https://transformer-circuits.pub/2022/toy_model/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/toy_model/index.html</guid>
      <pubDate>Wed, 31 Aug 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Softmax Linear Units</title>
      <description>Paper: An alternative activation function increases the fraction of neurons which appear to correspond to human-understandable concepts.</description>
      <link>https://transformer-circuits.pub/2022/solu/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/solu/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases</title>
      <description>Note: An informal note on intuitions related to mechanistic interpretability.</description>
      <link>https://transformer-circuits.pub/2022/mech-interp-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/mech-interp-essay/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>In-Context Learning and Induction Heads</title>
      <description>Paper: An exploration of the hypothesis that induction heads are the primary mechanism behind in-context learning. We also report the existence of a previously unknown phase change in transformers language models.</description>
      <link>https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</guid>
      <pubDate>Mon, 28 Feb 2022 16:00:00 GMT</pubDate>
      <author>Olsson et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>A Mathematical Framework for Transformer Circuits</title>
      <description>Paper: Our early mathematical framework for reverse engineering models, demonstrated by reverse engineering small toy models.</description>
      <link>https://transformer-circuits.pub/2021/framework/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/framework/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2021</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Exercises</title>
      <description>Note: Some exercises we&#39;ve developed to improve our understanding of how neural networks implement algorithms at the parameter level.</description>
      <link>https://transformer-circuits.pub/2021/exercises/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/exercises/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Videos</title>
      <description>Note: Very rough informal talks as we search for a way to reverse engineering transformers.</description>
      <link>https://transformer-circuits.pub/2021/videos/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/videos/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>PySvelte</title>
      <description>Note: One approach to bridging Python and web-based interactive diagrams for interpretability research.</description>
      <link>https://github.com/anthropics/PySvelte</link>
      <guid isPermaLink="false">https://github.com/anthropics/PySvelte</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Garcon</title>
      <description>Note: A description of our tooling for doing interpretability on large models.</description>
      <link>https://transformer-circuits.pub/2021/garcon/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/garcon/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Original Distill Circuits Thread</title>
      <description>Paper: Our exploration of Transformers builds heavily on the original Circuits thread on Distill.</description>
      <link>https://distill.pub/2020/circuits/</link>
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      <pubDate>Sat, 29 Feb 2020 16:00:00 GMT</pubDate>
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</details>

author_association: CONTRIBUTOR
user: login: github-actions[bot]
created_at: 2025-03-29T12:40:36Z
body: Successfully [generated](https://github.com/DIYgod/RSSHub/actions/runs/14145707779) as following:

<details>
<summary><a href="http://localhost:1200/transformer-circuits">http://localhost:1200/transformer-circuits</a> - Success ✔️</summary>

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      <title>On the Biology of a Large Language Model</title>
      <description>Paper: We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic&#39;s lightweight production model — in a variety of contexts.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/biology.html</link>
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      <category>Anthropic</category>
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      <description>Paper: We describe an approach to tracing the &quot;step-by-step&quot; computation involved when a model responds to a single prompt.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/methods.html</link>
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      <category>Anthropic</category>
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      <title>Stage-Wise Model Diffing</title>
      <description>Note: A preliminary note on model diffing through dictionary fine-tuning.</description>
      <link>https://transformer-circuits.pub/2024/model-diffing/index.html</link>
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      <pubDate>Sat, 30 Nov 2024 16:00:00 GMT</pubDate>
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      <category>Transformer Circuits</category>
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    <item>
      <title>Sparse Crosscoders for Cross-Layer Features and Model Diffing</title>
      <description>Note: A preliminary note on a way to get consistent features across layers, and even models.</description>
      <link>https://transformer-circuits.pub/2024/crosscoders/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/crosscoders/index.html</guid>
      <pubDate>Mon, 30 Sep 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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      <title>Using Dictionary Learning Features as Classifiers</title>
      <description>Note: A preliminary note comparing feature-based and raw-activation based harmfulness classifiers.</description>
      <link>https://transformer-circuits.pub/2024/features-as-classifiers/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/features-as-classifiers/index.html</guid>
      <pubDate>Mon, 30 Sep 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — September 2024</title>
      <description>Note: A collection of small updates: investigating successor heads, oversampling data in SAEs.</description>
      <link>https://transformer-circuits.pub/2024/september-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/september-update/index.html</guid>
      <pubDate>Sat, 31 Aug 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — August 2024</title>
      <description>Note: A collection of small updates: interpretability evals, reproducing self-explanation.</description>
      <link>https://transformer-circuits.pub/2024/august-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/august-update/index.html</guid>
      <pubDate>Wed, 31 Jul 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2024</title>
      <description>Note: A collection of small updates: five hurdles, linear representations, dark matter, pivot tables, feature sensitivity.</description>
      <link>https://transformer-circuits.pub/2024/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/july-update/index.html</guid>
      <pubDate>Sun, 30 Jun 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — June 2024</title>
      <description>Note: A collection of small updates: topk and gated SAE investigation.</description>
      <link>https://transformer-circuits.pub/2024/june-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/june-update/index.html</guid>
      <pubDate>Fri, 31 May 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from Claude 3 Sonnet. Some appear to be safety-relevant.</description>
      <link>https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</guid>
      <pubDate>Tue, 30 Apr 2024 16:00:00 GMT</pubDate>
      <author>Templeton et al., 2024</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Circuits Updates — April 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/april-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/april-update/index.html</guid>
      <pubDate>Sun, 31 Mar 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — March 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/march-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/march-update/index.html</guid>
      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Reflections on Qualitative Research</title>
      <description>Note: Some opinionated thoughts on why interpretability research may have qualitative aspects be more central than we&#39;re used to in other fields.</description>
      <link>https://transformer-circuits.pub/2024/qualitative-essay/index.html</link>
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      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — February 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/feb-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/feb-update/index.html</guid>
      <pubDate>Wed, 31 Jan 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — January 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/jan-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/jan-update/index.html</guid>
      <pubDate>Sun, 31 Dec 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Towards Monosemanticity: Decomposing Language Models With Dictionary Learning</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer.</description>
      <link>https://transformer-circuits.pub/2023/monosemantic-features/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/monosemantic-features/index.html</guid>
      <pubDate>Sat, 30 Sep 2023 16:00:00 GMT</pubDate>
      <author>Bricken et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/july-update/index.html</guid>
      <pubDate>Fri, 30 Jun 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — May 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/may-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/may-update/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Interpretability Dreams</title>
      <description>Note: Our present research aims to create a foundation for mechanistic interpretability research. In doing so, it&#39;s important to keep sight of what we&#39;re trying to lay the foundations for.</description>
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      <guid isPermaLink="false">https://transformer-circuits.pub/2023/interpretability-dreams/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Distributed Representations: Composition &amp; Superposition</title>
      <description>Note: An informal note on how &quot;distributed representations&quot; might be understood as two different, competing strategies — &quot;composition&quot; and &quot;superposition&quot; — with quite different properties.</description>
      <link>https://transformer-circuits.pub/2023/superposition-composition/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/superposition-composition/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Privileged Bases in the Transformer Residual Stream</title>
      <description>Paper: Our mathematical theories of the Transformer architecture suggest that
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        significance, but recent work has shown that this observation is false in practice.
        We investigate this phenomenon and provisionally conclude that the per-dimension normalizers in the Adam optimizer are to blame for the effect.</description>
      <link>https://transformer-circuits.pub/2023/privileged-basis/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/privileged-basis/index.html</guid>
      <pubDate>Tue, 28 Feb 2023 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Superposition, Memorization, and Double Descent</title>
      <description>Paper: We have little mechanistic understanding of how deep learning
        models overfit to their training data, despite it being a
        central problem. Here we extend our previous work on toy models
        to shed light on how models generalize beyond their training
        data.</description>
      <link>https://transformer-circuits.pub/2023/toy-double-descent/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/toy-double-descent/index.html</guid>
      <pubDate>Sat, 31 Dec 2022 16:00:00 GMT</pubDate>
      <author>Henighan et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Toy Models of Superposition</title>
      <description>Paper: Neural networks often seem to pack many unrelated concepts into a single neuron - a puzzling phenomenon known as &#39;polysemanticity&#39;. In our latest interpretability work, we build toy models where the origins and dynamics of polysemanticity can be fully understood.</description>
      <link>https://transformer-circuits.pub/2022/toy_model/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/toy_model/index.html</guid>
      <pubDate>Wed, 31 Aug 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Softmax Linear Units</title>
      <description>Paper: An alternative activation function increases the fraction of neurons which appear to correspond to human-understandable concepts.</description>
      <link>https://transformer-circuits.pub/2022/solu/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/solu/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases</title>
      <description>Note: An informal note on intuitions related to mechanistic interpretability.</description>
      <link>https://transformer-circuits.pub/2022/mech-interp-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/mech-interp-essay/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>In-Context Learning and Induction Heads</title>
      <description>Paper: An exploration of the hypothesis that induction heads are the primary mechanism behind in-context learning. We also report the existence of a previously unknown phase change in transformers language models.</description>
      <link>https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</guid>
      <pubDate>Mon, 28 Feb 2022 16:00:00 GMT</pubDate>
      <author>Olsson et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>A Mathematical Framework for Transformer Circuits</title>
      <description>Paper: Our early mathematical framework for reverse engineering models, demonstrated by reverse engineering small toy models.</description>
      <link>https://transformer-circuits.pub/2021/framework/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/framework/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2021</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Exercises</title>
      <description>Note: Some exercises we&#39;ve developed to improve our understanding of how neural networks implement algorithms at the parameter level.</description>
      <link>https://transformer-circuits.pub/2021/exercises/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/exercises/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Videos</title>
      <description>Note: Very rough informal talks as we search for a way to reverse engineering transformers.</description>
      <link>https://transformer-circuits.pub/2021/videos/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/videos/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>PySvelte</title>
      <description>Note: One approach to bridging Python and web-based interactive diagrams for interpretability research.</description>
      <link>https://github.com/anthropics/PySvelte</link>
      <guid isPermaLink="false">https://github.com/anthropics/PySvelte</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
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      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Garcon</title>
      <description>Note: A description of our tooling for doing interpretability on large models.</description>
      <link>https://transformer-circuits.pub/2021/garcon/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/garcon/index.html</guid>
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      <title>Original Distill Circuits Thread</title>
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      <link>https://distill.pub/2020/circuits/</link>
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author_association: CONTRIBUTOR
user: login: github-actions[bot]
created_at: 2025-03-29T13:39:33Z
body: Successfully [generated](https://github.com/DIYgod/RSSHub/actions/runs/14146174235) as following:

<details>
<summary><a href="http://localhost:1200/transformer-circuits">http://localhost:1200/transformer-circuits</a> - Success ✔️</summary>

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      <title>On the Biology of a Large Language Model</title>
      <description>Paper: We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic&#39;s lightweight production model — in a variety of contexts.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/biology.html</link>
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      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Circuit Tracing: Revealing Computational Graphs in Language Models</title>
      <description>Paper: We describe an approach to tracing the &quot;step-by-step&quot; computation involved when a model responds to a single prompt.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/methods.html</link>
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      <title>Using Dictionary Learning Features as Classifiers</title>
      <description>Note: A preliminary note comparing feature-based and raw-activation based harmfulness classifiers.</description>
      <link>https://transformer-circuits.pub/2024/features-as-classifiers/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/features-as-classifiers/index.html</guid>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Circuits Updates — September 2024</title>
      <description>Note: A collection of small updates: investigating successor heads, oversampling data in SAEs.</description>
      <link>https://transformer-circuits.pub/2024/september-update/index.html</link>
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      <category>Anthropic</category>
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      <title>Circuits Updates — August 2024</title>
      <description>Note: A collection of small updates: interpretability evals, reproducing self-explanation.</description>
      <link>https://transformer-circuits.pub/2024/august-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/august-update/index.html</guid>
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    <item>
      <title>Circuits Updates — July 2024</title>
      <description>Note: A collection of small updates: five hurdles, linear representations, dark matter, pivot tables, feature sensitivity.</description>
      <link>https://transformer-circuits.pub/2024/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/july-update/index.html</guid>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — June 2024</title>
      <description>Note: A collection of small updates: topk and gated SAE investigation.</description>
      <link>https://transformer-circuits.pub/2024/june-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/june-update/index.html</guid>
      <pubDate>Fri, 31 May 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from Claude 3 Sonnet. Some appear to be safety-relevant.</description>
      <link>https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</guid>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Circuits Updates — April 2024</title>
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      <link>https://transformer-circuits.pub/2024/april-update/index.html</link>
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      <title>Circuits Updates — March 2024</title>
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      <link>https://transformer-circuits.pub/2024/march-update/index.html</link>
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      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
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      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Reflections on Qualitative Research</title>
      <description>Note: Some opinionated thoughts on why interpretability research may have qualitative aspects be more central than we&#39;re used to in other fields.</description>
      <link>https://transformer-circuits.pub/2024/qualitative-essay/index.html</link>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — February 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/feb-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/feb-update/index.html</guid>
      <pubDate>Wed, 31 Jan 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — January 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/jan-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/jan-update/index.html</guid>
      <pubDate>Sun, 31 Dec 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Towards Monosemanticity: Decomposing Language Models With Dictionary Learning</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer.</description>
      <link>https://transformer-circuits.pub/2023/monosemantic-features/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/monosemantic-features/index.html</guid>
      <pubDate>Sat, 30 Sep 2023 16:00:00 GMT</pubDate>
      <author>Bricken et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/july-update/index.html</guid>
      <pubDate>Fri, 30 Jun 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — May 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/may-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/may-update/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Interpretability Dreams</title>
      <description>Note: Our present research aims to create a foundation for mechanistic interpretability research. In doing so, it&#39;s important to keep sight of what we&#39;re trying to lay the foundations for.</description>
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      <category>Anthropic</category>
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      <title>Distributed Representations: Composition &amp; Superposition</title>
      <description>Note: An informal note on how &quot;distributed representations&quot; might be understood as two different, competing strategies — &quot;composition&quot; and &quot;superposition&quot; — with quite different properties.</description>
      <link>https://transformer-circuits.pub/2023/superposition-composition/index.html</link>
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      <category>Anthropic</category>
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        We investigate this phenomenon and provisionally conclude that the per-dimension normalizers in the Adam optimizer are to blame for the effect.</description>
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      <description>Paper: We have little mechanistic understanding of how deep learning
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        central problem. Here we extend our previous work on toy models
        to shed light on how models generalize beyond their training
        data.</description>
      <link>https://transformer-circuits.pub/2023/toy-double-descent/index.html</link>
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      <title>Toy Models of Superposition</title>
      <description>Paper: Neural networks often seem to pack many unrelated concepts into a single neuron - a puzzling phenomenon known as &#39;polysemanticity&#39;. In our latest interpretability work, we build toy models where the origins and dynamics of polysemanticity can be fully understood.</description>
      <link>https://transformer-circuits.pub/2022/toy_model/index.html</link>
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      <pubDate>Wed, 31 Aug 2022 16:00:00 GMT</pubDate>
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      <title>Softmax Linear Units</title>
      <description>Paper: An alternative activation function increases the fraction of neurons which appear to correspond to human-understandable concepts.</description>
      <link>https://transformer-circuits.pub/2022/solu/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/solu/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
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      <category>AI</category>
      <category>Machine Learning</category>
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      <category>Transformer Circuits</category>
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    <item>
      <title>Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases</title>
      <description>Note: An informal note on intuitions related to mechanistic interpretability.</description>
      <link>https://transformer-circuits.pub/2022/mech-interp-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/mech-interp-essay/index.html</guid>
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      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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      <title>In-Context Learning and Induction Heads</title>
      <description>Paper: An exploration of the hypothesis that induction heads are the primary mechanism behind in-context learning. We also report the existence of a previously unknown phase change in transformers language models.</description>
      <link>https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</link>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
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      <title>A Mathematical Framework for Transformer Circuits</title>
      <description>Paper: Our early mathematical framework for reverse engineering models, demonstrated by reverse engineering small toy models.</description>
      <link>https://transformer-circuits.pub/2021/framework/index.html</link>
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      <title>Exercises</title>
      <description>Note: Some exercises we&#39;ve developed to improve our understanding of how neural networks implement algorithms at the parameter level.</description>
      <link>https://transformer-circuits.pub/2021/exercises/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/exercises/index.html</guid>
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      <title>Videos</title>
      <description>Note: Very rough informal talks as we search for a way to reverse engineering transformers.</description>
      <link>https://transformer-circuits.pub/2021/videos/index.html</link>
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      <category>Anthropic</category>
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      <title>PySvelte</title>
      <description>Note: One approach to bridging Python and web-based interactive diagrams for interpretability research.</description>
      <link>https://github.com/anthropics/PySvelte</link>
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      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Garcon</title>
      <description>Note: A description of our tooling for doing interpretability on large models.</description>
      <link>https://transformer-circuits.pub/2021/garcon/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/garcon/index.html</guid>
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      <category>AI</category>
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      <category>Anthropic</category>
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      <title>Original Distill Circuits Thread</title>
      <description>Paper: Our exploration of Transformers builds heavily on the original Circuits thread on Distill.</description>
      <link>https://distill.pub/2020/circuits/</link>
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      <pubDate>Sat, 29 Feb 2020 16:00:00 GMT</pubDate>
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author_association: CONTRIBUTOR
user: login: github-actions[bot]
created_at: 2025-03-29T14:15:53Z
body: Successfully [generated](https://github.com/DIYgod/RSSHub/actions/runs/14146429273) as following:

<details>
<summary><a href="http://localhost:1200/transformer-circuits">http://localhost:1200/transformer-circuits</a> - Success ✔️</summary>

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      <title>On the Biology of a Large Language Model</title>
      <description>Paper: We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic&#39;s lightweight production model — in a variety of contexts.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/biology.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/attribution-graphs/biology.html</guid>
      <pubDate>Fri, 28 Feb 2025 16:00:00 GMT</pubDate>
      <author>Lindsey et al., 2025</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuit Tracing: Revealing Computational Graphs in Language Models</title>
      <description>Paper: We describe an approach to tracing the &quot;step-by-step&quot; computation involved when a model responds to a single prompt.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/methods.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/attribution-graphs/methods.html</guid>
      <pubDate>Fri, 28 Feb 2025 16:00:00 GMT</pubDate>
      <author>Ameisen et al., 2025</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Insights on Crosscoder Model Diffing</title>
      <description>Note: A preliminary note on using crosscoders to diff models.</description>
      <link>https://transformer-circuits.pub/2025/crosscoder-diffing-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/crosscoder-diffing-update/index.html</guid>
      <pubDate>Fri, 31 Jan 2025 16:00:00 GMT</pubDate>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Circuits Updates — January 2025</title>
      <description>Note: A collection of small updates: dictionary learning optimization techniques.</description>
      <link>https://transformer-circuits.pub/2025/january-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2025/january-update/index.html</guid>
      <pubDate>Tue, 31 Dec 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Stage-Wise Model Diffing</title>
      <description>Note: A preliminary note on model diffing through dictionary fine-tuning.</description>
      <link>https://transformer-circuits.pub/2024/model-diffing/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/model-diffing/index.html</guid>
      <pubDate>Sat, 30 Nov 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Sparse Crosscoders for Cross-Layer Features and Model Diffing</title>
      <description>Note: A preliminary note on a way to get consistent features across layers, and even models.</description>
      <link>https://transformer-circuits.pub/2024/crosscoders/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/crosscoders/index.html</guid>
      <pubDate>Mon, 30 Sep 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Using Dictionary Learning Features as Classifiers</title>
      <description>Note: A preliminary note comparing feature-based and raw-activation based harmfulness classifiers.</description>
      <link>https://transformer-circuits.pub/2024/features-as-classifiers/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/features-as-classifiers/index.html</guid>
      <pubDate>Mon, 30 Sep 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — September 2024</title>
      <description>Note: A collection of small updates: investigating successor heads, oversampling data in SAEs.</description>
      <link>https://transformer-circuits.pub/2024/september-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/september-update/index.html</guid>
      <pubDate>Sat, 31 Aug 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — August 2024</title>
      <description>Note: A collection of small updates: interpretability evals, reproducing self-explanation.</description>
      <link>https://transformer-circuits.pub/2024/august-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/august-update/index.html</guid>
      <pubDate>Wed, 31 Jul 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2024</title>
      <description>Note: A collection of small updates: five hurdles, linear representations, dark matter, pivot tables, feature sensitivity.</description>
      <link>https://transformer-circuits.pub/2024/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/july-update/index.html</guid>
      <pubDate>Sun, 30 Jun 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — June 2024</title>
      <description>Note: A collection of small updates: topk and gated SAE investigation.</description>
      <link>https://transformer-circuits.pub/2024/june-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/june-update/index.html</guid>
      <pubDate>Fri, 31 May 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from Claude 3 Sonnet. Some appear to be safety-relevant.</description>
      <link>https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</guid>
      <pubDate>Tue, 30 Apr 2024 16:00:00 GMT</pubDate>
      <author>Templeton et al., 2024</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — April 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/april-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/april-update/index.html</guid>
      <pubDate>Sun, 31 Mar 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — March 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/march-update/index.html</link>
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      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Reflections on Qualitative Research</title>
      <description>Note: Some opinionated thoughts on why interpretability research may have qualitative aspects be more central than we&#39;re used to in other fields.</description>
      <link>https://transformer-circuits.pub/2024/qualitative-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/qualitative-essay/index.html</guid>
      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — February 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/feb-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/feb-update/index.html</guid>
      <pubDate>Wed, 31 Jan 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — January 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/jan-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/jan-update/index.html</guid>
      <pubDate>Sun, 31 Dec 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Towards Monosemanticity: Decomposing Language Models With Dictionary Learning</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer.</description>
      <link>https://transformer-circuits.pub/2023/monosemantic-features/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/monosemantic-features/index.html</guid>
      <pubDate>Sat, 30 Sep 2023 16:00:00 GMT</pubDate>
      <author>Bricken et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/july-update/index.html</guid>
      <pubDate>Fri, 30 Jun 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — May 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/may-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/may-update/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Interpretability Dreams</title>
      <description>Note: Our present research aims to create a foundation for mechanistic interpretability research. In doing so, it&#39;s important to keep sight of what we&#39;re trying to lay the foundations for.</description>
      <link>https://transformer-circuits.pub/2023/interpretability-dreams/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/interpretability-dreams/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Distributed Representations: Composition &amp; Superposition</title>
      <description>Note: An informal note on how &quot;distributed representations&quot; might be understood as two different, competing strategies — &quot;composition&quot; and &quot;superposition&quot; — with quite different properties.</description>
      <link>https://transformer-circuits.pub/2023/superposition-composition/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/superposition-composition/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Privileged Bases in the Transformer Residual Stream</title>
      <description>Paper: Our mathematical theories of the Transformer architecture suggest that
        individual coordinates in the residual stream should have no special
        significance, but recent work has shown that this observation is false in practice.
        We investigate this phenomenon and provisionally conclude that the per-dimension normalizers in the Adam optimizer are to blame for the effect.</description>
      <link>https://transformer-circuits.pub/2023/privileged-basis/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/privileged-basis/index.html</guid>
      <pubDate>Tue, 28 Feb 2023 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Superposition, Memorization, and Double Descent</title>
      <description>Paper: We have little mechanistic understanding of how deep learning
        models overfit to their training data, despite it being a
        central problem. Here we extend our previous work on toy models
        to shed light on how models generalize beyond their training
        data.</description>
      <link>https://transformer-circuits.pub/2023/toy-double-descent/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/toy-double-descent/index.html</guid>
      <pubDate>Sat, 31 Dec 2022 16:00:00 GMT</pubDate>
      <author>Henighan et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Toy Models of Superposition</title>
      <description>Paper: Neural networks often seem to pack many unrelated concepts into a single neuron - a puzzling phenomenon known as &#39;polysemanticity&#39;. In our latest interpretability work, we build toy models where the origins and dynamics of polysemanticity can be fully understood.</description>
      <link>https://transformer-circuits.pub/2022/toy_model/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/toy_model/index.html</guid>
      <pubDate>Wed, 31 Aug 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Softmax Linear Units</title>
      <description>Paper: An alternative activation function increases the fraction of neurons which appear to correspond to human-understandable concepts.</description>
      <link>https://transformer-circuits.pub/2022/solu/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/solu/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases</title>
      <description>Note: An informal note on intuitions related to mechanistic interpretability.</description>
      <link>https://transformer-circuits.pub/2022/mech-interp-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/mech-interp-essay/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>In-Context Learning and Induction Heads</title>
      <description>Paper: An exploration of the hypothesis that induction heads are the primary mechanism behind in-context learning. We also report the existence of a previously unknown phase change in transformers language models.</description>
      <link>https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</guid>
      <pubDate>Mon, 28 Feb 2022 16:00:00 GMT</pubDate>
      <author>Olsson et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>A Mathematical Framework for Transformer Circuits</title>
      <description>Paper: Our early mathematical framework for reverse engineering models, demonstrated by reverse engineering small toy models.</description>
      <link>https://transformer-circuits.pub/2021/framework/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/framework/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2021</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Exercises</title>
      <description>Note: Some exercises we&#39;ve developed to improve our understanding of how neural networks implement algorithms at the parameter level.</description>
      <link>https://transformer-circuits.pub/2021/exercises/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/exercises/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Videos</title>
      <description>Note: Very rough informal talks as we search for a way to reverse engineering transformers.</description>
      <link>https://transformer-circuits.pub/2021/videos/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/videos/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>PySvelte</title>
      <description>Note: One approach to bridging Python and web-based interactive diagrams for interpretability research.</description>
      <link>https://github.com/anthropics/PySvelte</link>
      <guid isPermaLink="false">https://github.com/anthropics/PySvelte</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Garcon</title>
      <description>Note: A description of our tooling for doing interpretability on large models.</description>
      <link>https://transformer-circuits.pub/2021/garcon/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/garcon/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Original Distill Circuits Thread</title>
      <description>Paper: Our exploration of Transformers builds heavily on the original Circuits thread on Distill.</description>
      <link>https://distill.pub/2020/circuits/</link>
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      <pubDate>Sat, 29 Feb 2020 16:00:00 GMT</pubDate>
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</details>

author_association: CONTRIBUTOR
user: login: github-actions[bot]
created_at: 2025-03-30T13:51:36Z
body: Successfully [generated](https://github.com/DIYgod/RSSHub/actions/runs/14156670101) as following:

<details>
<summary><a href="http://localhost:1200/transformer-circuits">http://localhost:1200/transformer-circuits</a> - Success ✔️</summary>

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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Sparse Crosscoders for Cross-Layer Features and Model Diffing</title>
      <description>Note: A preliminary note on a way to get consistent features across layers, and even models.</description>
      <link>https://transformer-circuits.pub/2024/crosscoders/index.html</link>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Using Dictionary Learning Features as Classifiers</title>
      <description>Note: A preliminary note comparing feature-based and raw-activation based harmfulness classifiers.</description>
      <link>https://transformer-circuits.pub/2024/features-as-classifiers/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/features-as-classifiers/index.html</guid>
      <pubDate>Mon, 30 Sep 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — September 2024</title>
      <description>Note: A collection of small updates: investigating successor heads, oversampling data in SAEs.</description>
      <link>https://transformer-circuits.pub/2024/september-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/september-update/index.html</guid>
      <pubDate>Sat, 31 Aug 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — August 2024</title>
      <description>Note: A collection of small updates: interpretability evals, reproducing self-explanation.</description>
      <link>https://transformer-circuits.pub/2024/august-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/august-update/index.html</guid>
      <pubDate>Wed, 31 Jul 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2024</title>
      <description>Note: A collection of small updates: five hurdles, linear representations, dark matter, pivot tables, feature sensitivity.</description>
      <link>https://transformer-circuits.pub/2024/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/july-update/index.html</guid>
      <pubDate>Sun, 30 Jun 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — June 2024</title>
      <description>Note: A collection of small updates: topk and gated SAE investigation.</description>
      <link>https://transformer-circuits.pub/2024/june-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/june-update/index.html</guid>
      <pubDate>Fri, 31 May 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from Claude 3 Sonnet. Some appear to be safety-relevant.</description>
      <link>https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</guid>
      <pubDate>Tue, 30 Apr 2024 16:00:00 GMT</pubDate>
      <author>Templeton et al., 2024</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — April 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/april-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/april-update/index.html</guid>
      <pubDate>Sun, 31 Mar 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — March 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/march-update/index.html</link>
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      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Reflections on Qualitative Research</title>
      <description>Note: Some opinionated thoughts on why interpretability research may have qualitative aspects be more central than we&#39;re used to in other fields.</description>
      <link>https://transformer-circuits.pub/2024/qualitative-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/qualitative-essay/index.html</guid>
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      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Circuits Updates — February 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/feb-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/feb-update/index.html</guid>
      <pubDate>Wed, 31 Jan 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — January 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/jan-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/jan-update/index.html</guid>
      <pubDate>Sun, 31 Dec 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Towards Monosemanticity: Decomposing Language Models With Dictionary Learning</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer.</description>
      <link>https://transformer-circuits.pub/2023/monosemantic-features/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/monosemantic-features/index.html</guid>
      <pubDate>Sat, 30 Sep 2023 16:00:00 GMT</pubDate>
      <author>Bricken et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/july-update/index.html</guid>
      <pubDate>Fri, 30 Jun 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — May 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/may-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/may-update/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Interpretability Dreams</title>
      <description>Note: Our present research aims to create a foundation for mechanistic interpretability research. In doing so, it&#39;s important to keep sight of what we&#39;re trying to lay the foundations for.</description>
      <link>https://transformer-circuits.pub/2023/interpretability-dreams/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/interpretability-dreams/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Distributed Representations: Composition &amp; Superposition</title>
      <description>Note: An informal note on how &quot;distributed representations&quot; might be understood as two different, competing strategies — &quot;composition&quot; and &quot;superposition&quot; — with quite different properties.</description>
      <link>https://transformer-circuits.pub/2023/superposition-composition/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/superposition-composition/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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      <title>Privileged Bases in the Transformer Residual Stream</title>
      <description>Paper: Our mathematical theories of the Transformer architecture suggest that
        individual coordinates in the residual stream should have no special
        significance, but recent work has shown that this observation is false in practice.
        We investigate this phenomenon and provisionally conclude that the per-dimension normalizers in the Adam optimizer are to blame for the effect.</description>
      <link>https://transformer-circuits.pub/2023/privileged-basis/index.html</link>
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      <pubDate>Tue, 28 Feb 2023 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2023</author>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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      <title>Superposition, Memorization, and Double Descent</title>
      <description>Paper: We have little mechanistic understanding of how deep learning
        models overfit to their training data, despite it being a
        central problem. Here we extend our previous work on toy models
        to shed light on how models generalize beyond their training
        data.</description>
      <link>https://transformer-circuits.pub/2023/toy-double-descent/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/toy-double-descent/index.html</guid>
      <pubDate>Sat, 31 Dec 2022 16:00:00 GMT</pubDate>
      <author>Henighan et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Toy Models of Superposition</title>
      <description>Paper: Neural networks often seem to pack many unrelated concepts into a single neuron - a puzzling phenomenon known as &#39;polysemanticity&#39;. In our latest interpretability work, we build toy models where the origins and dynamics of polysemanticity can be fully understood.</description>
      <link>https://transformer-circuits.pub/2022/toy_model/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/toy_model/index.html</guid>
      <pubDate>Wed, 31 Aug 2022 16:00:00 GMT</pubDate>
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      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
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      <title>Softmax Linear Units</title>
      <description>Paper: An alternative activation function increases the fraction of neurons which appear to correspond to human-understandable concepts.</description>
      <link>https://transformer-circuits.pub/2022/solu/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/solu/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases</title>
      <description>Note: An informal note on intuitions related to mechanistic interpretability.</description>
      <link>https://transformer-circuits.pub/2022/mech-interp-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/mech-interp-essay/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>In-Context Learning and Induction Heads</title>
      <description>Paper: An exploration of the hypothesis that induction heads are the primary mechanism behind in-context learning. We also report the existence of a previously unknown phase change in transformers language models.</description>
      <link>https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</guid>
      <pubDate>Mon, 28 Feb 2022 16:00:00 GMT</pubDate>
      <author>Olsson et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>A Mathematical Framework for Transformer Circuits</title>
      <description>Paper: Our early mathematical framework for reverse engineering models, demonstrated by reverse engineering small toy models.</description>
      <link>https://transformer-circuits.pub/2021/framework/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/framework/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2021</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Exercises</title>
      <description>Note: Some exercises we&#39;ve developed to improve our understanding of how neural networks implement algorithms at the parameter level.</description>
      <link>https://transformer-circuits.pub/2021/exercises/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/exercises/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Videos</title>
      <description>Note: Very rough informal talks as we search for a way to reverse engineering transformers.</description>
      <link>https://transformer-circuits.pub/2021/videos/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/videos/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>PySvelte</title>
      <description>Note: One approach to bridging Python and web-based interactive diagrams for interpretability research.</description>
      <link>https://github.com/anthropics/PySvelte</link>
      <guid isPermaLink="false">https://github.com/anthropics/PySvelte</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Garcon</title>
      <description>Note: A description of our tooling for doing interpretability on large models.</description>
      <link>https://transformer-circuits.pub/2021/garcon/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/garcon/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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    <item>
      <title>Original Distill Circuits Thread</title>
      <description>Paper: Our exploration of Transformers builds heavily on the original Circuits thread on Distill.</description>
      <link>https://distill.pub/2020/circuits/</link>
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      <pubDate>Sat, 29 Feb 2020 16:00:00 GMT</pubDate>
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</details>

author_association: CONTRIBUTOR
user: login: github-actions[bot]
created_at: 2025-03-30T13:57:25Z
body: Successfully [generated](https://github.com/DIYgod/RSSHub/actions/runs/14156710821) as following:

<details>
<summary><a href="http://localhost:1200/transformer-circuits">http://localhost:1200/transformer-circuits</a> - Success ✔️</summary>

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      <description>Paper: We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic&#39;s lightweight production model — in a variety of contexts.</description>
      <link>https://transformer-circuits.pub/2025/attribution-graphs/biology.html</link>
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      <link>https://transformer-circuits.pub/2025/attribution-graphs/methods.html</link>
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      <category>Machine Learning</category>
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      <category>Machine Learning</category>
      <category>Anthropic</category>
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      <description>Note: A collection of small updates: interpretability evals, reproducing self-explanation.</description>
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    <item>
      <title>Circuits Updates — July 2024</title>
      <description>Note: A collection of small updates: five hurdles, linear representations, dark matter, pivot tables, feature sensitivity.</description>
      <link>https://transformer-circuits.pub/2024/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/july-update/index.html</guid>
      <pubDate>Sun, 30 Jun 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — June 2024</title>
      <description>Note: A collection of small updates: topk and gated SAE investigation.</description>
      <link>https://transformer-circuits.pub/2024/june-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/june-update/index.html</guid>
      <pubDate>Fri, 31 May 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from Claude 3 Sonnet. Some appear to be safety-relevant.</description>
      <link>https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html</guid>
      <pubDate>Tue, 30 Apr 2024 16:00:00 GMT</pubDate>
      <author>Templeton et al., 2024</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — April 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/april-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/april-update/index.html</guid>
      <pubDate>Sun, 31 Mar 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — March 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/march-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/march-update/index.html</guid>
      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Reflections on Qualitative Research</title>
      <description>Note: Some opinionated thoughts on why interpretability research may have qualitative aspects be more central than we&#39;re used to in other fields.</description>
      <link>https://transformer-circuits.pub/2024/qualitative-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/qualitative-essay/index.html</guid>
      <pubDate>Thu, 29 Feb 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — February 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/feb-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/feb-update/index.html</guid>
      <pubDate>Wed, 31 Jan 2024 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — January 2024</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2024/jan-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2024/jan-update/index.html</guid>
      <pubDate>Sun, 31 Dec 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Towards Monosemanticity: Decomposing Language Models With Dictionary Learning</title>
      <description>Paper: Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer.</description>
      <link>https://transformer-circuits.pub/2023/monosemantic-features/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/monosemantic-features/index.html</guid>
      <pubDate>Sat, 30 Sep 2023 16:00:00 GMT</pubDate>
      <author>Bricken et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — July 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/july-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/july-update/index.html</guid>
      <pubDate>Fri, 30 Jun 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Circuits Updates — May 2023</title>
      <description>Note: A collection of small updates from the Anthropic Interpretability Team.</description>
      <link>https://transformer-circuits.pub/2023/may-update/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/may-update/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Interpretability Dreams</title>
      <description>Note: Our present research aims to create a foundation for mechanistic interpretability research. In doing so, it&#39;s important to keep sight of what we&#39;re trying to lay the foundations for.</description>
      <link>https://transformer-circuits.pub/2023/interpretability-dreams/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/interpretability-dreams/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Distributed Representations: Composition &amp; Superposition</title>
      <description>Note: An informal note on how &quot;distributed representations&quot; might be understood as two different, competing strategies — &quot;composition&quot; and &quot;superposition&quot; — with quite different properties.</description>
      <link>https://transformer-circuits.pub/2023/superposition-composition/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/superposition-composition/index.html</guid>
      <pubDate>Sun, 30 Apr 2023 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Privileged Bases in the Transformer Residual Stream</title>
      <description>Paper: Our mathematical theories of the Transformer architecture suggest that
        individual coordinates in the residual stream should have no special
        significance, but recent work has shown that this observation is false in practice.
        We investigate this phenomenon and provisionally conclude that the per-dimension normalizers in the Adam optimizer are to blame for the effect.</description>
      <link>https://transformer-circuits.pub/2023/privileged-basis/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/privileged-basis/index.html</guid>
      <pubDate>Tue, 28 Feb 2023 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Superposition, Memorization, and Double Descent</title>
      <description>Paper: We have little mechanistic understanding of how deep learning
        models overfit to their training data, despite it being a
        central problem. Here we extend our previous work on toy models
        to shed light on how models generalize beyond their training
        data.</description>
      <link>https://transformer-circuits.pub/2023/toy-double-descent/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2023/toy-double-descent/index.html</guid>
      <pubDate>Sat, 31 Dec 2022 16:00:00 GMT</pubDate>
      <author>Henighan et al., 2023</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Toy Models of Superposition</title>
      <description>Paper: Neural networks often seem to pack many unrelated concepts into a single neuron - a puzzling phenomenon known as &#39;polysemanticity&#39;. In our latest interpretability work, we build toy models where the origins and dynamics of polysemanticity can be fully understood.</description>
      <link>https://transformer-circuits.pub/2022/toy_model/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/toy_model/index.html</guid>
      <pubDate>Wed, 31 Aug 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Softmax Linear Units</title>
      <description>Paper: An alternative activation function increases the fraction of neurons which appear to correspond to human-understandable concepts.</description>
      <link>https://transformer-circuits.pub/2022/solu/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/solu/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases</title>
      <description>Note: An informal note on intuitions related to mechanistic interpretability.</description>
      <link>https://transformer-circuits.pub/2022/mech-interp-essay/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/mech-interp-essay/index.html</guid>
      <pubDate>Tue, 31 May 2022 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>In-Context Learning and Induction Heads</title>
      <description>Paper: An exploration of the hypothesis that induction heads are the primary mechanism behind in-context learning. We also report the existence of a previously unknown phase change in transformers language models.</description>
      <link>https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html</guid>
      <pubDate>Mon, 28 Feb 2022 16:00:00 GMT</pubDate>
      <author>Olsson et al., 2022</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>A Mathematical Framework for Transformer Circuits</title>
      <description>Paper: Our early mathematical framework for reverse engineering models, demonstrated by reverse engineering small toy models.</description>
      <link>https://transformer-circuits.pub/2021/framework/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/framework/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <author>Elhage et al., 2021</author>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Exercises</title>
      <description>Note: Some exercises we&#39;ve developed to improve our understanding of how neural networks implement algorithms at the parameter level.</description>
      <link>https://transformer-circuits.pub/2021/exercises/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/exercises/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Videos</title>
      <description>Note: Very rough informal talks as we search for a way to reverse engineering transformers.</description>
      <link>https://transformer-circuits.pub/2021/videos/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/videos/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>PySvelte</title>
      <description>Note: One approach to bridging Python and web-based interactive diagrams for interpretability research.</description>
      <link>https://github.com/anthropics/PySvelte</link>
      <guid isPermaLink="false">https://github.com/anthropics/PySvelte</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Garcon</title>
      <description>Note: A description of our tooling for doing interpretability on large models.</description>
      <link>https://transformer-circuits.pub/2021/garcon/index.html</link>
      <guid isPermaLink="false">https://transformer-circuits.pub/2021/garcon/index.html</guid>
      <pubDate>Tue, 30 Nov 2021 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
    </item>
    <item>
      <title>Original Distill Circuits Thread</title>
      <description>Paper: Our exploration of Transformers builds heavily on the original Circuits thread on Distill.</description>
      <link>https://distill.pub/2020/circuits/</link>
      <guid isPermaLink="false">https://distill.pub/2020/circuits/</guid>
      <pubDate>Sat, 29 Feb 2020 16:00:00 GMT</pubDate>
      <category>AI</category>
      <category>Machine Learning</category>
      <category>Anthropic</category>
      <category>Transformer Circuits</category>
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```
</details>

author_association: CONTRIBUTOR
user: login: github-actions[bot]
created_at: 2025-03-30T14:24:16Z
body: Successfully [generated](https://github.com/DIYgod/RSSHub/actions/runs/14156923822) as following:

<details>
<summary><a href="http://localhost:1200/transformer-circuits">http://localhost:1200/transformer-circuits</a> - Success ✔️</summary>

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    <title>Transformer Circuits Thread</title>
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    <item>
      <title>On the Biology of a Large Language Model</title>
      <description>&lt;div class=&quot;content-wrapper&quot;&gt;
        &lt;d-contents&gt;
        &lt;nav class=&quot;l-text toc figcaption&quot;&gt;
        &lt;h3&gt;Contents&lt;/h3&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#introduction&quot;&gt;Introduction&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;Method Overview&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;Multi-step Reasoning&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poems&quot;&gt;Planning in Poems&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-multilingual&quot;&gt;Multilingual Circuits&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-addition&quot;&gt;Addition&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-medical&quot;&gt;Medical Diagnoses&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-hallucinations&quot;&gt;Entity Recognition and Hallucinations&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-refusals&quot;&gt;Refusals&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-jailbreak&quot;&gt;Life of a Jailbreak&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-cot&quot;&gt;Chain-of-thought Faithfulness&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-misaligned&quot;&gt;Uncovering Hidden Goals in a Misaligned Model&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#structure&quot;&gt;Commonly Observed Circuit Components and Structure&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;Limitations&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#discussion&quot;&gt;Discussion&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#related-work&quot;&gt;Related Work&lt;/a&gt;&lt;/div&gt;
        &lt;div style=&quot;width: 100%; max-width: 50px; border-top: 1px solid #CCC; margin-top: 12px; margin-bottom: 12px;&quot;&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#acknowledgments&quot;&gt;Acknowledgments&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#author-contributions&quot;&gt;Author Contributions&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#citation-info&quot;&gt;Citation Information&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;Open Questions&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#appendix-tokens&quot;&gt;Special Tokens&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#appendix-interactive-vis&quot;&gt;Graph Pruning &amp;amp; Visualization&lt;/a&gt;&lt;/div&gt;
        &lt;/nav&gt;
        &lt;/d-contents&gt;
        &lt;div class=&quot;invisible-introduction&quot;&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;introduction&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#introduction&quot;&gt;Introduction&lt;/a&gt;&lt;/h2&gt;
        &lt;/div&gt;
        &lt;p&gt;Large language models display impressive capabilities. However, for the most part, the mechanisms by which they do so are unknown. The black-box nature of models is increasingly unsatisfactory as they advance in intelligence and are deployed in a growing number of applications. Our goal is to reverse engineer how these models work on the inside, so we may better understand them and assess their fitness for purpose.&lt;/p&gt;
        &lt;p&gt;The challenges we face in understanding language models resemble those faced by biologists. Living organisms are complex systems which have been sculpted by billions of years of evolution. While the basic principles of evolution are straightforward, the biological mechanisms it produces are spectacularly intricate. Likewise, while language models are generated by simple, human-designed training algorithms, the &lt;span style=&quot;font-style: italic;&quot;&gt;mechanisms &lt;/span&gt;born of these algorithms appear to be quite complex.&lt;/p&gt;
        &lt;p&gt;Progress in biology is often driven by new tools. The development of the microscope allowed scientists to see cells for the first time, revealing a new world of structures invisible to the naked eye. In recent years, many research groups have made exciting progress on tools for probing the insides of language models (&lt;span style=&quot;font-style: italic;&quot;&gt;e.g.&lt;/span&gt;&amp;nbsp;&lt;d-cite key=&quot;cunningham2023sparse,bricken2023monosemanticity,templeton2024scaling,gao2024scaling,dunefsky2024transcoders&quot;&gt;&lt;/d-cite&gt;). These methods have uncovered representations of interpretable concepts – “features” – embedded within models’ internal activity.&amp;nbsp;Just as cells form the building blocks of biological systems, we hypothesize that features form the basic units of computation inside models.&lt;d-footnote&gt;The analogy between features and cells shouldn’t be taken too literally. Cells are well-defined, whereas our notion of what exactly a “feature” is remains fuzzy, and is evolving with improvements to our tools.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;p&gt;However, identifying these building blocks is not sufficient to understand the model; we need to know how they interact. In our companion paper, &lt;span style=&quot;font-style: italic;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;Circuit Tracing: Revealing Computational Graphs in Language Models&lt;/a&gt;&lt;/span&gt;, we build on recent work (e.g. &lt;d-cite key=&quot;dunefsky2024transcoders,marks2024sparse,ge2024automatically,lindsey2024crosscoders&quot;&gt;&lt;/d-cite&gt;) to introduce a new set of tools for identifying features and mapping connections between them – analogous to neuroscientists producing a “wiring diagram” of the brain. We rely heavily on a tool we call &lt;span style=&quot;font-style: italic;&quot;&gt;attribution graphs&lt;/span&gt;, which allow us to partially trace the chain of intermediate steps that a model uses to transform a specific input prompt into an output response. Attribution graphs generate hypotheses about the mechanisms used by the model, which we test and refine through follow-up perturbation experiments.&lt;/p&gt;
        &lt;p&gt;In this paper, we focus on applying attribution graphs to study a particular language model – Claude 3.5 Haiku, released in October 2024, which serves as Anthropic’s lightweight production model as of this writing. We investigate a wide range of phenomena. Many of these have been explored before (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#related-work&quot;&gt;§&amp;nbsp;Related Work&lt;/a&gt;), but our methods are able to offer additional insight, in the context of a frontier model:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;Introductory Example: Multi-step Reasoning.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We present a simple example where the model performs “two-hop” reasoning&amp;nbsp;“in its head” to identify that “the capital of the state containing Dallas” is “Austin.” We can see and manipulate an internal step where the model represents “Texas”.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poems&quot;&gt;Planning in Poems.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We discover that&amp;nbsp;the model plans its outputs ahead of time when writing lines of poetry. Before beginning to write each line, the model identifies potential rhyming words that could appear at the end. These preselected rhyming options then shape how the model constructs the entire line.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-multilingual&quot;&gt;Multilingual Circuits.&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&amp;nbsp;&lt;/span&gt;We find the model uses a mixture of language-specific and abstract, language-independent circuits. The language-independent circuits&amp;nbsp;are more prominent in Claude 3.5 Haiku than in&amp;nbsp;a smaller, less capable model.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-addition&quot;&gt;Addition.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We highlight cases where the same addition circuitry generalizes between very different contexts.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-medical&quot;&gt;Medical &lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-medical&quot;&gt;Diagnoses&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;. &lt;/span&gt;We show an example in which the model identifies candidate diagnoses based on reported symptoms, and uses these to inform follow-up questions about additional symptoms that could corroborate the diagnosis – all “in its head,” without writing down its steps.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-hallucinations&quot;&gt;Entity Recognition and Hallucinations.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We uncover circuit mechanisms that allow the model to distinguish between familiar and unfamiliar entities, which determine whether it elects to answer a factual question or profess ignorance. “Misfires” of this circuit can cause hallucinations.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-refusals&quot;&gt;Refusal of Harmful Requests.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We find evidence that the model constructs a general-purpose “harmful requests” feature during finetuning, aggregated from features representing &lt;span style=&quot;font-style: italic;&quot;&gt;specific &lt;/span&gt;harmful requests learned during pretraining.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-jailbreak&quot;&gt;An Analysis of a Jailbreak.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We investigate an attack which works by first tricking the model into starting to give dangerous instructions “without realizing it,” after which it continues to do so due to pressure to adhere to syntactic and grammatical rules.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-cot&quot;&gt;Chain-of-thought Faithfulness.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We explore the faithfulness of chain-of-thought&amp;nbsp;reasoning to the model’s actual mechanisms. We are able to distinguish between cases where the model genuinely performs the steps it says it is performing, cases where it makes up its reasoning without regard for truth, and cases where it &lt;span style=&quot;font-style: italic;&quot;&gt;works backwards &lt;/span&gt;from a human-provided clue so that its “reasoning” will end up at the human-suggested answer.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-misaligned&quot;&gt;A Model with a Hidden Goal.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We also apply our method to a variant of the model that has been finetuned to pursue a secret goal: exploiting “bugs” in its training process. While the model avoids revealing its goal when asked, our method identifies mechanisms involved in pursuing the goal. Interestingly, these mechanisms are embedded within the model’s representation of its “Assistant” persona.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;Our results uncover a variety of sophisticated strategies employed by models. For instance, Claude 3.5 Haiku routinely uses multiple intermediate reasoning steps “in its head”&lt;d-footnote&gt;That is, during the forward pass rather than the &quot;thinking out loud&quot; of a chain-of-thought completion.&lt;/d-footnote&gt; to decide its outputs. It displays signs of &lt;span style=&quot;font-style: italic;&quot;&gt;forward planning&lt;/span&gt;, considering multiple possibilities for what it will say well in advance of saying it. It performs &lt;span style=&quot;font-style: italic;&quot;&gt;backward planning,&lt;/span&gt;&amp;nbsp;working backwards from goal states to formulate earlier parts of its response. We see signs of primitive “metacognitive” circuits that allow the model to know the extent of its own knowledge. More broadly, the model’s internal computations are highly abstract and generalize across disparate contexts. Our methods are also sometimes capable of auditing a model’s internal reasoning steps to flag concerning “thought processes” that are not clear from the model’s responses.&lt;/p&gt;
        &lt;p&gt;Below, we present:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;brief overview&lt;/a&gt;&amp;nbsp;of our methodology (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;the companion paper&lt;/a&gt;&amp;nbsp;for more details on our methods). &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;introductory case study&lt;/a&gt;, which also serves as a walkthrough for understanding our approach. Readers who have not read our companion paper may find it helpful to begin with this section before proceeding to the other case studies.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives&quot;&gt;series of case studies&lt;/a&gt;&amp;nbsp;of interesting model behaviors, &lt;span style=&quot;font-style: italic;&quot;&gt;which can be read in any order, depending on the reader’s interests&lt;/span&gt;. &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A summary of &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#structure&quot;&gt;common components&lt;/a&gt;&amp;nbsp;observed across our investigations.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A description of gaps in our understanding that motivate future work (&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;§&amp;nbsp;&lt;/a&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;Limitations&lt;/a&gt;).&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A discussion of high-level takeaways about models, their mechanisms, and our methods for studying them (&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#discussion&quot;&gt;§&amp;nbsp;Discussion&lt;/a&gt;). This includes a &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#discussion-unsupervised&quot;&gt;note&lt;/a&gt;&amp;nbsp;on our research philosophy – in particular, the value of tools for &lt;span style=&quot;font-style: italic;&quot;&gt;bottom-up &lt;/span&gt;investigation, which allow us to avoid making strong top-down guesses about how models work.&lt;/li&gt;&lt;/ul&gt;
        &lt;h3&gt;A note on our approach and its limitations&lt;/h3&gt;
        &lt;p&gt;Like any microscope, our tools are limited in what they can see. Though it’s difficult to quantify precisely, we’ve found that our attribution graphs provide us with satisfying insight for about a quarter of the prompts we’ve tried (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;§&amp;nbsp;Limitations&lt;/a&gt;&amp;nbsp;for a more detailed discussion of when our methods are likely to succeed or fail).&amp;nbsp;The examples we highlight are success cases where we have managed to learn something interesting; moreover, even in our successful case studies, &lt;span style=&quot;font-style: italic;&quot;&gt;the discoveries we highlight here only capture a small fraction of the mechanisms of the model&lt;/span&gt;. Our methods study the model indirectly using a more interpretable “replacement model,” which incompletely and imperfectly captures the original. Moreover, for the sake of clear communication, we will often present highly distilled and subjectively determined simplifications&amp;nbsp;of the picture uncovered by our methods, losing even more information in the process. To provide a more accurate sense of the rich complexity we have uncovered, we provide readers with an interactive interface for exploring attribution graphs. However, we stress that even these rather complex graphs are simplifications of the underlying model.&lt;/p&gt;
        &lt;p&gt;We focus this paper on selected case studies that illuminate noteworthy mechanisms within a particular model. These examples serve as existence proofs —&amp;nbsp;concrete evidence that specific mechanisms operate in certain contexts. While we suspect similar mechanisms are at play beyond these examples, we cannot guarantee it (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;§&amp;nbsp;&lt;/a&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;Open &lt;/a&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;Questions&lt;/a&gt;&amp;nbsp;for suggested follow-up investigations). Moreover, the cases we have chosen to highlight are undoubtedly a biased sample shaped by the limitations of our tools.&lt;d-footnote&gt;However, we are careful to stress-test our findings with follow-up validation experiments, which we have endeavored to perform only after identifying case studies of interest.&lt;/d-footnote&gt; For a more systematic evaluation of our methods, see our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;companion paper&lt;/a&gt;. However, we believe that these qualitative investigations are ultimately the best judge of a method’s value, just as the usefulness of a microscope is ultimately determined by the scientific discoveries it enables. We expect this kind of work will be essential to advance the current state of AI interpretability, a pre-paradigmatic field still in search of the right abstractions — just as descriptive science has proven essential to many conceptual breakthroughs in biology. We are particularly excited that squeezing as much insight as we can out of our current methods has brought into clearer focus their specific &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;limitations&lt;/a&gt;, which may serve as a roadmap for future research in the field.&lt;/p&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;method-overview&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;Method Overview&lt;/a&gt;&lt;/h2&gt;
        &lt;p&gt;The models we study in this work are &lt;span style=&quot;font-weight: 700;&quot;&gt;transformer-based language models,&lt;/span&gt;&amp;nbsp;which take in sequences of tokens (e.g. words, word fragments, and special characters), and output new tokens one at a time. These models involve two fundamental components – &lt;span style=&quot;font-style: italic;&quot;&gt;MLP (“multi-layer perceptron”) layers&lt;/span&gt;, which process information within each token position using collections of &lt;span style=&quot;font-style: italic;&quot;&gt;neurons&lt;/span&gt;; and &lt;span style=&quot;font-style: italic;&quot;&gt;attention layers&lt;/span&gt;, which move information between token positions. &lt;/p&gt;
        &lt;p&gt;One reason models are difficult to interpret is that their neurons are typically &lt;span style=&quot;font-style: italic;&quot;&gt;polysemantic&lt;/span&gt;&amp;nbsp;– that is, they perform many different functions that are seemingly unrelated.&lt;d-footnote&gt;This issue is thought to arise in part because of a phenomenon known as &lt;span style=&quot;font-style: italic;&quot;&gt;superposition&lt;/span&gt;&amp;nbsp;&lt;d-cite key=&quot;arora2018linear,goh2016decoding,olah2020zoom,elhage2022superposition&quot;&gt;&lt;/d-cite&gt;, whereby models represent more concepts than they have neurons and thus cannot assign each neuron to its own concept.&lt;/d-footnote&gt; To circumvent this issue, we build a &lt;span style=&quot;font-weight: 700;&quot;&gt;replacement model&lt;/span&gt;&amp;nbsp;that approximately reproduces the activations of the original model using more interpretable components. Our replacement model is based on a &lt;span style=&quot;font-style: italic;&quot;&gt;cross-layer transcoder&lt;/span&gt;&amp;nbsp;(CLT) architecture&amp;nbsp;(see &lt;d-cite key=&quot;lindsey2024crosscoders&quot;&gt;&lt;/d-cite&gt; and our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;companion methods paper&lt;/a&gt;), which is trained to replace the model’s MLP neurons with &lt;span style=&quot;font-weight: 700;&quot;&gt;features,&lt;/span&gt;&amp;nbsp;sparsely active “replacement neurons” that often represent interpretable concepts. In this paper, we use a CLT with a total of 30 million features across all layers.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-1-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Features often represent human-interpretable concepts, ranging from low-level (e.g. specific words or phrases) to high-level (e.g. sentiments, plans, and reasoning steps). By examining a &lt;span style=&quot;font-weight: 700;&quot;&gt;feature visualization &lt;/span&gt;consisting of different examples of text where the feature activates, we can give each feature a human-interpretable label. Note that the text examples in this paper are taken from open source datasets.&lt;/p&gt;
        &lt;p&gt;Our replacement models don’t perfectly reconstruct the activations of the original model. On any given prompt, there are gaps between the two. We can fill in these gaps by including &lt;span style=&quot;font-weight: 700;&quot;&gt;error nodes&lt;/span&gt;&amp;nbsp;which represent the discrepancy between the two models. Unlike features, we can’t interpret error nodes. But including them gives us a more precise sense of how incomplete our explanations are. Our replacement model also doesn’t attempt to replace the attention layers of the original model. On any given prompt, we simply use the attention patterns of the original model and treat them as fixed components.&lt;/p&gt;
        &lt;p&gt;The resulting model – incorporating error nodes and inheriting the attention patterns from the original model – we call the &lt;span style=&quot;font-weight: 700;&quot;&gt;local replacement model&lt;/span&gt;.&amp;nbsp;It is “local” to a given prompt because error nodes and attention patterns vary between different prompts. But it still represents as much of the original model’s computation as possible using (somewhat) interpretable features.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-2-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;By studying the interactions between features in the local replacement model, we can trace its intermediate steps as it produces responses. More concretely, we produce &lt;span style=&quot;font-weight: 700;&quot;&gt;attribution graphs&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;,&lt;/span&gt;&amp;nbsp;a graphical representation of the computational steps the model&amp;nbsp;uses to determine its output for a particular input, in which nodes represent features and edges represent the causal interactions between them. As attribution graphs can be quite complex, we &lt;span style=&quot;font-style: italic;&quot;&gt;prune&lt;/span&gt;&amp;nbsp;them to their most important components by removing nodes and edges that do not contribute significantly to the model’s output.&lt;/p&gt;
        &lt;p&gt;With a pruned attribution graph in hand, we often observe groups of features with related meanings that play a similar role in the graph. By manually grouping these related graph nodes together into &lt;span style=&quot;font-weight: 700;&quot;&gt;supernodes,&lt;/span&gt;&amp;nbsp;we can obtain a simplified depiction of the computational steps performed by the model.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-3-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;These simplified diagrams form the centerpiece of many of our case studies. Below (left) we show an example of such a diagram.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-4-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Because they are based on our &lt;span style=&quot;font-style: italic;&quot;&gt;replacement model&lt;/span&gt;, we cannot use attribution graphs to draw conclusions with certainty about the &lt;span style=&quot;font-style: italic;&quot;&gt;underlying model &lt;/span&gt;(i.e. Claude 3.5 Haiku). &amp;nbsp;Thus, the attribution graphs provide &lt;span style=&quot;font-style: italic;&quot;&gt;hypotheses&lt;/span&gt;&amp;nbsp;about mechanisms operating in the underlying model. For a discussion of when and why these hypotheses might be incomplete or misleading, see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;§&amp;nbsp;Limitations&lt;/a&gt;. To gain confidence that the mechanisms we describe are real and significant, we can perform &lt;span style=&quot;font-style: italic;&quot;&gt;intervention&lt;/span&gt;&amp;nbsp;experiments in the original model, such as inhibiting feature groups and observing their effects on other features and on the model’s output (final figure panel above – percentages indicate fraction of original activation). If the effects are consistent with what our attribution graph predicts, we gain confidence that the graph is capturing real (though potentially incomplete) mechanisms within the model. Importantly, we choose our feature labelings and supernode groupings prior to measuring perturbation results.&amp;nbsp;Note that there are some nuances in interpreting the results of intervention experiments, and the extent to which they provide independent validation of graph-predicted mechanisms – see our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html#graphs-interventions&quot;&gt;companion paper&lt;/a&gt;&amp;nbsp;for further details.&lt;d-footnote&gt;Performing interventions with cross-layer transcoder features requires choosing an “intervention layer,” with the perturbation applied up to that layer. &amp;nbsp;Our interventions in this paper use the “constrained patching” technique described in our companion paper, which clamps activations prior to the intervention layer at perturbed values, preventing any &lt;span style=&quot;font-style: italic;&quot;&gt;indirect&lt;/span&gt;&amp;nbsp;effects of the perturbation from manifesting prior to the intervention layer. Thus, effects of perturbations on features prior to the intervention layer are guaranteed to agree with the &lt;span style=&quot;font-style: italic;&quot;&gt;direct&lt;/span&gt;&amp;nbsp;effects predicted by the attribution graph. By contrast, perturbation effects on features &lt;span style=&quot;font-style: italic;&quot;&gt;after&lt;/span&gt;&amp;nbsp;the intervention layer have the potential to diverge from graph predictions, in two ways: (1) The graph-predicted &lt;span style=&quot;font-style: italic;&quot;&gt;direct &lt;/span&gt;effects may be overwhelmed by other mechanisms missed by our attribution graphs, (2) The graph-predicted &lt;span style=&quot;font-style: italic;&quot;&gt;indirect&lt;/span&gt;&amp;nbsp;effects (i.e. ‘multi-hop’ interactions) may not even exist within the underlying model (we refer to this issue as “mechanistic unfaithfulness”). Thus, the nature of the validation provided by our intervention experiments varies depending on the layers of the features involved and the directness of their interaction in the attribution graph, and in some cases (direct effects prior to the intervention layer) is trivial. In general, we regard the effects of interventions on the model’s &lt;span style=&quot;font-style: italic;&quot;&gt;actual outputs&lt;/span&gt;&amp;nbsp;as the most important source of validation, as model outputs are simple to interpret and not affected by these methodological artifacts.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;p&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;Alongside each case study figure, we provide the &lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;interactive attribution graph interface&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;detail-graph-button-inline&quot;&gt;&lt;/span&gt; &lt;span style=&quot;font-style: italic;&quot;&gt;that our team uses to study models’ internal mechanisms. The interface is designed to enable “tracing” key paths through the graph while labeling key features, feature groups, and subcircuits. The interface is fairly complex and takes some time to become proficient at using. All the key results in this work are described and visualized in simplified form, so that engaging with this interface is not necessary to read the paper! However, we recommend giving it a try if you are interested in gaining a richer sense of the mechanisms at play in Claude 3.5 Haiku. Some features are given brief labels for convenience; these labels are very rough interpretations and miss considerable detail, which can be better appreciated in the feature visualizations. For a more detailed walkthrough, please reference &lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html#graphs-tutorial&quot;&gt;this section&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;in our companion methods paper&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;(and see &lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#appendix-interactive-vis&quot;&gt;§&amp;nbsp;Appendix: Graph Pruning and Visualization&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;for a few methodological differences specific to this paper).&lt;/span&gt;&lt;/p&gt;
        &lt;p&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&lt;/span&gt;&lt;/p&gt;
        &lt;!--The following is to keep old links working--&gt; &lt;div id=&quot;dives&quot;&gt;&lt;/div&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;dives-tracing&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;Introductory Example: Multi-step Reasoning&lt;/a&gt;&lt;/h2&gt;
        &lt;p&gt;Our methodology is intended to expose the intermediate steps a model uses en route to producing a response. In this section we consider a simple example of multi-step reasoning and attempt to identify each step. Along the way, we will highlight key concepts that will appear in many of our other case studies.&lt;/p&gt;
        &lt;p&gt;Let’s consider the prompt &lt;span class=&quot;prompt-inline&quot;&gt;Fact: the capital of the state containing Dallas is&lt;/span&gt;, which Claude 3.5 Haiku successfully completes with &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt;. Intuitively, this completion requires two steps – first, inferring that the state containing Dallas is Texas, and second, that the capital of Texas is Austin. Does Claude actually perform these two steps internally? &amp;nbsp;Or does it use some “shortcut” (e.g. perhaps it has observed a similar sentence in the training data and simply memorized the completion)? &amp;nbsp;Prior work &lt;d-cite key=&quot;yang2024large,yu2025back,biran2024hopping&quot;&gt;&lt;/d-cite&gt; has shown evidence of genuine multi-hop reasoning (to varying degrees in different contexts).&lt;/p&gt;
        &lt;p&gt;In this section we provide evidence that,&amp;nbsp;in this example, the model performs genuine two-step reasoning internally, which coexists alongside “shortcut” reasoning.&lt;/p&gt;
        &lt;p&gt;As described in the &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;method overview&lt;/a&gt;, we can tackle this question by computing the &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/static_js/attribution_graphs/index.html?slug=capital-state-dallas&quot;&gt;attribution graph&lt;/a&gt;&amp;nbsp;for this prompt, which describes the features the model used to produce its answer, and the interactions between them. First, we examine the features’ visualizations to interpret them, and group them into categories (“supernodes”).&amp;nbsp;For example:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We find several features about the word and/or concept of a capital city, such as four features that activate the most strongly on the &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b1729136_b21747437_b12394058_b3719952&quot;&gt;exact word “capital”&lt;/span&gt;. More interestingly, we find features that represent the concept of capitals in more general ways. One example is &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b10714744&quot;&gt;this feature&lt;/span&gt;, which activates on the word “capitals” but also later in questions about capitals of states, as well as on the Chinese question &lt;span class=&quot;prompt-inline&quot;&gt;广东省的省&lt;b class=&quot;max-act&quot;&gt;会&lt;/b&gt;是？&lt;/span&gt; (“What is the capital of Guangdong?”), on the second character of “省会” (capital of a province). Another is this &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b10502022&quot;&gt;multilingual feature&lt;/span&gt;, which activates most strongly on a variety of phrases including “başkenti”, “राजधानी”, “ibu kota”, and “Hauptftadt” — all of which roughly mean “capital” in different languages.&lt;d-footnote&gt;Though much of the German is mangled, perhaps due to a transcription error in the source.&lt;/d-footnote&gt; Although each of these features represents slightly different concepts, in the context of this prompt, it seems like their function is&amp;nbsp;to represent&amp;nbsp;the idea of “capital.” Therefore, we group them (and a few others) together into the same “supernode.”&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We also identify “output features” that consistently push the model to say certain tokens, even if there isn’t such a clear pattern to what words/phrases they activate on. This can be seen in the “Top Outputs” section of a feature’s visualization, which lists the output tokens most strongly &lt;span style=&quot;font-style: italic;&quot;&gt;directly&lt;/span&gt;&amp;nbsp;upweighted by that feature. For example, one&amp;nbsp;feature&amp;nbsp;activates on various &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b21064819&quot;&gt;landmarks in central Texas&lt;/span&gt;, but in this prompt its most relevant aspect is that it most strongly promotes responding with the “Austin” token. We therefore categorize this feature into a “say Austin” supernode. Note that the “Top Outputs” information is not always informative – for instance, earlier-layer features primarily matter via &lt;span style=&quot;font-style: italic;&quot;&gt;indirect&lt;/span&gt;&amp;nbsp;effects on the output via other features, and their top &lt;span style=&quot;font-style: italic;&quot;&gt;direct&lt;/span&gt;&amp;nbsp;outputs are not too consequential. Designating a feature as an “output feature” requires a holistic evaluation of its top direct outputs, the contexts in which it activates, and its role in the attribution graph.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We also find features that promote outputting the name of a capital more generally, which we use a mix of both types of signals to identify and label. For example, one&amp;nbsp;feature&amp;nbsp;promotes &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19290335&quot;&gt;responding with a variety of U.S. state capitals&lt;/span&gt;. Another&amp;nbsp;feature&amp;nbsp;more strongly &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b1826808&quot;&gt;promotes the capitals of various countries&lt;/span&gt;&amp;nbsp;rather than U.S. states, but activates most strongly on lists with U.S. states and their capitals. And we noticed another feature whose strongest direct outputs are a seemingly unrelated set of tokens, but which often activates right &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b26759354&quot;&gt;before a country capital&lt;/span&gt;&amp;nbsp;(e.g., Paris, Warsaw, or Canberra). We group all these features into a “say a capital” supernode.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We find several&amp;nbsp;features representing a variety of contexts &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4949911_b21568836_b29910101_b10643859_b2269103_b15049231_b1425335_b26101674_b15514815_b2447901_b6939384_b19260881&quot;&gt;relating to the state of Texas&lt;/span&gt;, not specific to a particular city&amp;nbsp;(in particular, they are not “Dallas” or “Austin” features). &amp;nbsp;Although they each represent distinct, specific Texas-related concepts, in the context of this prompt their main function appears to be the fact that they collectively represent the general concept of Texas. As such, we group these into a “Texas” supernode.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;After forming these supernodes, we can see in our attribution graph interface that, for example, the “capital” supernode promotes the “say a capital” supernode, which promotes the “say Austin” supernode. To represent this, we draw a diagram where each supernode is connected to the next with a brown arrow, as in the below graph snippet:&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;dallas-capital-tutorial-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;After labeling more features and forming more supernodes, we summarize their interactions in the following diagram.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;dallas-capitial-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The attribution graph contains&amp;nbsp;multiple interesting paths, which we summarize below:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;The &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b783162_b7304301_b21862382_b13686348_b22886075_b16536263_b23133920_b1348861_b27636071_b4418612&quot;&gt;Dallas&lt;/span&gt;&amp;nbsp;features (with some contribution from &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b5859881_b3631215_b19791479_b24231446_b5588528&quot;&gt;state&lt;/span&gt;&amp;nbsp;features) activate a group of features that represent concepts related to the state of Texas. &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;In parallel, the features activated by the word &lt;span class=&quot;prompt-inline&quot;&gt;capital&lt;/span&gt; activate another cluster of output features that cause the model to &lt;span style=&quot;font-style: italic;&quot;&gt;say&lt;/span&gt;&amp;nbsp;the name of a capital (an example of such a feature can be seen above). &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;The &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4949911_b21568836_b29910101_b10643859_b2269103_b15049231_b1425335_b26101674_b15514815_b2447901_b6939384_b19260881&quot;&gt;Texas&lt;/span&gt;&amp;nbsp;features&amp;nbsp;and the &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19290335_b12505127_b1826808_b1881152_b25477698_b26759354&quot;&gt;say a capital&lt;/span&gt;&amp;nbsp;features jointly upweight the probability of the model saying &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt;. They do so via two pathways:&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 72pt;&quot;&gt;directly impacting the &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt; output, and&lt;/li&gt;&lt;li style=&quot;margin-left: 72pt;&quot;&gt;indirectly, by activating a cluster of &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b21064819_b17926448_b8513782_b19214798_b2811388_b19253009&quot;&gt;say Austin&lt;/span&gt;&amp;nbsp;output features.&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;There also exists a “shortcut” edge directly&amp;nbsp;from &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b783162_b7304301_b21862382_b13686348_b22886075_b16536263_b23133920_b1348861_b27636071_b4418612&quot;&gt;Dallas&lt;/span&gt;&amp;nbsp;to &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b21064819_b17926448_b8513782_b19214798_b2811388_b19253009&quot;&gt;say Austin&lt;/span&gt;.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;The graph indicates that the replacement model does in fact perform “multi-hop reasoning” – that is, its decision to say &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt; hinges on a chain of several intermediate computational steps (Dallas → Texas, and Texas + capital → Austin). We stress that this graph simplifies the true mechanisms considerably, and encourage the reader to interact with the &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/static_js/attribution_graphs/index.html?slug=capital-state-dallas&quot;&gt;more comprehensive visualization&lt;/a&gt;&amp;nbsp;to appreciate the underlying complexity. &lt;/p&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-tracing-inhibition&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing-inhibition&quot;&gt;Validation with Inhibition Experiments&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;The graphs above describe mechanisms used by our interpretable replacement model. To validate that these mechanisms are representative of the &lt;span style=&quot;font-style: italic;&quot;&gt;actual&lt;/span&gt;&amp;nbsp;model, we performed intervention experiments on the feature groups above by inhibiting each of them (clamping them to a negative multiple of their original value&amp;nbsp;– see our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html#appendix-unexplained-var&quot;&gt;companion paper&lt;/a&gt;&amp;nbsp;for discussion of the choice of intervention strengths) and measuring the impact on the activations of features in the other clusters, as well as on the model output.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;capitals-ablate-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The summary plot above confirms the major effects predicted by the graph. For instance, inhibiting “Dallas” features decreases the activation of “Texas” features (and features downstream of “Texas,” like “Say Austin”) but leaves “say a capital” features largely unaffected. Likewise, inhibiting “capital” features decreases the activation of “say a capital” features (and those downstream, like “say Austin”) while leaving “Texas” features largely unchanged.&lt;/p&gt;
        &lt;p&gt;The effects of inhibiting features on model predictions are also semantically reasonable. For instance, inhibiting the “Dallas” cluster causes the model to output other state capitals, while inhibiting the “say a capital” cluster causes it to output non-capital completions.&lt;/p&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-tracing-swap&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing-swap&quot;&gt;Swapping Alternative Features&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;If the model’s completion truly is mediated by an intermediate “Texas” step, we should be able to change its output to a different state capital by replacing the model’s representation of Texas with that of another state. &lt;/p&gt;
        &lt;p&gt;To identify features representing another state, we consider a related prompt, where we use “Oakland” instead of “Dallas” – &lt;span class=&quot;prompt-inline&quot;&gt;Fact: the capital of the state containing Oakland is&lt;/span&gt;. Repeating the analysis steps above, we arrive at the following summary graph:&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;oakland-capitial-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;This graph is analogous to our original graph, with “Oakland” taking the place of “Dallas,” “California” taking the place of “Texas,” and “say Sacramento” taking the place of “say Austin.”&lt;/p&gt;
        &lt;p&gt;We now return to our original prompt, and swap “Texas” for “California” by inhibiting the activations of the Texas cluster and activating the &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b18287696_b11785424_b12492732_b27078162_b17186919&quot;&gt;California&lt;/span&gt;&amp;nbsp;features identified from the “Oakland” prompt. In response to these perturbations, the model outputs “Sacramento” (the capital of California).&lt;/p&gt;
        &lt;p&gt;Similarly,&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the state containing Savannah&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b15985777_b6427073_b3049042_b1328451_b26599100_b4558003_b30597962&quot;&gt;“Georgia”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Atlanta” (the capital of Georgia).&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the province containing Vancouver&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b30461587_b1726334_b17150480_b29375216_b9185546_b30421753_b7272359_b6739158_b23328299_b9651621&quot;&gt;“British Columbia”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Victoria” (the capital of British Columbia).&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the country containing Shanghai&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b12868051_b17928811_b2280931_b7142797_b1056609_b7123361&quot;&gt;“China”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Beijing” (the capital of China).&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the empire containing Thessaloniki&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b6362538_b8403831_b13852269_b14504952_b8220179_b13378317&quot;&gt;“Byzantine Empire”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Constantinople” (the capital of the ancient Byzantine Empire).&lt;/li&gt;&lt;/ul&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;capitals-swap-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Note that in some cases the magnitude of the feature injection required to change the model’s output is larger (see bottom row). Interestingly, these correspond to cases where the features being injected do not correspond to a U.S. state, suggesting that these features may “fit” less naturally into the circuit mechanisms active in the original prompt.&lt;/p&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;dives-poems&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poems&quot;&gt;Planning in Poems&lt;/a&gt;&lt;/h2&gt;
        &lt;p&gt;How does Claude 3.5 Haiku write a rhyming poem?&amp;nbsp;Writing a poem requires satisfying two constraints at the same time: the lines need to rhyme, and they need to make sense. There are two ways one might imagine a model achieving this:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;Pure improvisation – &lt;/span&gt;the model could write the beginning of each line without regard for the need to rhyme at the end. Then, at the last word of each line, it would choose a word that (1) makes sense given the line it has just written, and (2) fits the rhyme scheme.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;Planning&lt;/span&gt;&amp;nbsp;– alternatively, the model could pursue a more sophisticated strategy. At the &lt;span style=&quot;font-style: italic;&quot;&gt;beginning &lt;/span&gt;of each line, it could come up with the word it plans to use at the end, taking into account the rhyme scheme and the content of the previous lines. It could then use this “planned word” to inform how it writes the next line, so that the planned word will fit naturally at the end of it.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;Language models are trained to predict the next word, one word at a time. Given this, one might think the model would rely on pure improvisation. However, we find compelling evidence for a planning mechanism. &lt;/p&gt;
        &lt;p&gt;Specifically, the model often activates features corresponding to candidate end-of-next-line words prior to writing the line, and makes use of these features to decide how to compose the line.&lt;d-footnote&gt;We found planned word features in about half of the poems we investigated, which may be due to our CLT not capturing features for the planned words, or it may be the case that the model does not always engage in planning.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poems-hero-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Prior work has observed evidence of planning in language models and other sequence models (&lt;span style=&quot;font-style: italic;&quot;&gt;e.g.&lt;/span&gt;&amp;nbsp;&lt;d-cite key=&quot;taufeeque2024planning,bush2025interpreting,jenner2025evidence&quot;&gt;&lt;/d-cite&gt; in games and &lt;d-cite key=&quot;pal2023future,wu2024language,pochinkov2025planpara&quot;&gt;&lt;/d-cite&gt;; see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#related-work&quot;&gt;§&amp;nbsp;Related Work&lt;/a&gt;). Our example adds to this body of evidence, and is particularly striking in several ways:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We provide a &lt;span style=&quot;font-style: italic;&quot;&gt;mechanistic&lt;/span&gt;&amp;nbsp;account of how planned words are computed and used downstream. &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We find evidence of both &lt;span style=&quot;font-style: italic;&quot;&gt;forward planning&lt;/span&gt;&amp;nbsp;and &lt;span style=&quot;font-style: italic;&quot;&gt;backwards planning&lt;/span&gt;&amp;nbsp;(albeit basic forms). First, the model uses the semantic and rhyming constraints of the poem to determine candidate targets for the next line. Next, the model works &lt;span style=&quot;font-style: italic;&quot;&gt;backward&lt;/span&gt;&amp;nbsp;from its target word to write a sentence that naturally ends in that word.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We observe that the model &lt;span style=&quot;font-style: italic;&quot;&gt;holds multiple possible planned words&lt;/span&gt;&amp;nbsp;“in mind” at the same time.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We are able to &lt;span style=&quot;font-style: italic;&quot;&gt;edit&lt;/span&gt;&amp;nbsp;the model’s planned word and see that it restructures its next line accordingly.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We discovered the mechanism with an unsupervised, bottom-up approach.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;The features used to represent planned words seem to be ordinary features representing that word, rather than planning-specific features. This suggests that the model “thinks about” planned words using representations that are similar to when it &lt;span style=&quot;font-style: italic;&quot;&gt;reads about&lt;/span&gt;&amp;nbsp;those words.&lt;/li&gt;&lt;/ul&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-planned-words&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-planned-words&quot;&gt;Planned Words Features and their Mechanistic Role&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;We study how Claude completes the following prompt asking for a rhyming couplet. The model’s output, sampling the most likely token at each step, is shown in bold:&lt;/p&gt;
        &lt;div class=&quot;ha-block&quot;&gt;&lt;p&gt;A rhyming couplet:
        &lt;/p&gt;&lt;p&gt;He saw a carrot and had to grab it,&lt;/p&gt;
        &lt;p&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;His hunger was like a starving rabbit&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&lt;/p&gt;
        &lt;p&gt;To start, we focus on the last word of the second line and attempt to identify the circuit which contributed to choosing “rabbit”. We initially hypothesized that we’d observe &lt;span style=&quot;font-style: italic;&quot;&gt;improvisation – &lt;/span&gt;a circuit where rhyming features and semantic features constructively interfere to promote “rabbit”. Instead, we found that important components of the circuit were localized on the new-line token &lt;span style=&quot;font-style: italic;&quot;&gt;before the second line began&lt;/span&gt;:&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;rabbit-rhyme-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The attribution graph above, computed by attributing back from the “rabbit” output node, shows an important group of features active on the newline token, before the beginning of the second line. Features active over the&amp;nbsp;&lt;code&gt;“it”&lt;/code&gt; token activate &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b6169984_b22764715&quot;&gt;rhyming with “eet/it/et”&lt;/span&gt;&amp;nbsp;features, which themselves activate features for candidate completions such as &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4348905_b20630267_b9660338_b7333549&quot;&gt;“rabbit”&lt;/span&gt;&amp;nbsp;and &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19521875_b1872824_b3606664_b25192077&quot;&gt;“habit”&lt;/span&gt;! The candidate completions in turn have positive edges to &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b14555052_b21446643_b1529687_b27749057_b19591632&quot;&gt;say “rabbit”&lt;/span&gt;&amp;nbsp;features&amp;nbsp;over the last token, eventually promoting the corresponding output token. This is consistent with the model planning potential completions ahead of time.&lt;d-footnote&gt;The full circuit shows a diversity of feature groups promoting earlier phonemes of potential completions, such as ones&amp;nbsp;for &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b23747281_b15037125_b28235495_b23937603_b21323253_b25818533_b30416633&quot;&gt;words which contain an “ab” sound&lt;/span&gt;&amp;nbsp;matching the one in “grab”.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;p&gt;To test this hypothesis, we perform a variety of interventions at the newline planning site, and see how it affects the probability of the last token. We suppress features for planned words and rhyming schemes, and also inject different rhyming schemes and planned words. &lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poetry-interventions-1-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The results confirm our hypothesis that these planning features strongly influence final token probabilities.&lt;/p&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-poem-location&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poem-location&quot;&gt;Planning Features Only Matter at the Planning Location&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;Circuit analysis suggests that planning occurs at the newline token.&lt;d-footnote&gt;In fact, the features are only active over the planning token.&lt;/d-footnote&gt; Below, we show the probability of predicting a different final token by varying which token we steer at. At each index, we steer negatively on &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4348905_b20630267_b9660338_b7333549&quot;&gt;“rabbit”&lt;/span&gt;&amp;nbsp;and &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19521875_b1872824_b3606664_b25192077&quot;&gt;“habit”&lt;/span&gt;&amp;nbsp;features, and positively on a &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b30112108&quot;&gt;“green”&lt;/span&gt;&amp;nbsp;feature.&lt;d-footnote&gt;The “green” feature is an equivalent planning feature which we found in a separate poem. It activates over the word green in a variety of contexts, including misspellings.&lt;/d-footnote&gt; We observe that the steering intervention only has an effect over the newline planning token!&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;steering-index-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-planned-intermediate&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-planned-intermediate&quot;&gt;Planned Words Influence Intermediate Words&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;Is the planning circuit only involved in choosing the final word, or does it also cause the model to &quot;write towards&quot; the target as it produces the intermediate words? To answer this question, we compute an attribution&amp;nbsp;graph for the intermediate word “like”. &lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;rabbit-like-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The same “rabbit” planning features are active in the graph, promoting a group of “comparison features”, which are active before text such as “like a”. The model goes from the planned target (“rabbit”) that activates at the newline&amp;nbsp;and reasons backwards, creating a plausible transition to get to it.&lt;/p&gt;
        &lt;p&gt;Again, we test this with interventions. We find that suppressing the “rabbit” features changes the transition to one that matches the alternate planned word, “habit”.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poetry-interventions-2b-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-planned-structure&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-planned-structure&quot;&gt;Planned Words Determine Sentence Structure&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;But do the planning features influence the &lt;span style=&quot;font-style: italic;&quot;&gt;entire&lt;/span&gt;&amp;nbsp;sentence structure? We test this with further steering experiments where we perform our earlier set of interventions, but resample the entire final line. We find that interventions on planning features cause the model to alter the structure of its line to reach a final word which matches the intervention.&lt;d-footnote&gt;We show example lines for the likeliest final words, as well as estimated probabilities for the final word based on 500 samples per experiment.&lt;/d-footnote&gt;.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poetry-interventions-2-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;In addition to the examples above, we injected two planned word features (“rabbit” and “green”) in a random sample of 25 poems, and found that the model ended its line with the injected planned word in 70% of cases. We show a few samples of success cases below.&lt;/p&gt;
        &lt;table&gt;&lt;thead&gt;&lt;tr&gt;&lt;td colspan=&quot;1&quot; rowspan=&quot;1&quot
```
</details>

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<details>
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      <title>On the Biology of a Large Language Model</title>
      <description>&lt;div class=&quot;content-wrapper&quot;&gt;
        &lt;d-contents&gt;
        &lt;nav class=&quot;l-text toc figcaption&quot;&gt;
        &lt;h3&gt;Contents&lt;/h3&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#introduction&quot;&gt;Introduction&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;Method Overview&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;Multi-step Reasoning&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poems&quot;&gt;Planning in Poems&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-multilingual&quot;&gt;Multilingual Circuits&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-addition&quot;&gt;Addition&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-medical&quot;&gt;Medical Diagnoses&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-hallucinations&quot;&gt;Entity Recognition and Hallucinations&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-refusals&quot;&gt;Refusals&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-jailbreak&quot;&gt;Life of a Jailbreak&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-cot&quot;&gt;Chain-of-thought Faithfulness&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-misaligned&quot;&gt;Uncovering Hidden Goals in a Misaligned Model&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#structure&quot;&gt;Commonly Observed Circuit Components and Structure&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;Limitations&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#discussion&quot;&gt;Discussion&lt;/a&gt;&lt;/div&gt;
        &lt;div&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#related-work&quot;&gt;Related Work&lt;/a&gt;&lt;/div&gt;
        &lt;div style=&quot;width: 100%; max-width: 50px; border-top: 1px solid #CCC; margin-top: 12px; margin-bottom: 12px;&quot;&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#acknowledgments&quot;&gt;Acknowledgments&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#author-contributions&quot;&gt;Author Contributions&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#citation-info&quot;&gt;Citation Information&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;Open Questions&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#appendix-tokens&quot;&gt;Special Tokens&lt;/a&gt;&lt;/div&gt;
        &lt;div class=&quot;appendix-toc&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#appendix-interactive-vis&quot;&gt;Graph Pruning &amp;amp; Visualization&lt;/a&gt;&lt;/div&gt;
        &lt;/nav&gt;
        &lt;/d-contents&gt;
        &lt;div class=&quot;invisible-introduction&quot;&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;introduction&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#introduction&quot;&gt;Introduction&lt;/a&gt;&lt;/h2&gt;
        &lt;/div&gt;
        &lt;p&gt;Large language models display impressive capabilities. However, for the most part, the mechanisms by which they do so are unknown. The black-box nature of models is increasingly unsatisfactory as they advance in intelligence and are deployed in a growing number of applications. Our goal is to reverse engineer how these models work on the inside, so we may better understand them and assess their fitness for purpose.&lt;/p&gt;
        &lt;p&gt;The challenges we face in understanding language models resemble those faced by biologists. Living organisms are complex systems which have been sculpted by billions of years of evolution. While the basic principles of evolution are straightforward, the biological mechanisms it produces are spectacularly intricate. Likewise, while language models are generated by simple, human-designed training algorithms, the &lt;span style=&quot;font-style: italic;&quot;&gt;mechanisms &lt;/span&gt;born of these algorithms appear to be quite complex.&lt;/p&gt;
        &lt;p&gt;Progress in biology is often driven by new tools. The development of the microscope allowed scientists to see cells for the first time, revealing a new world of structures invisible to the naked eye. In recent years, many research groups have made exciting progress on tools for probing the insides of language models (&lt;span style=&quot;font-style: italic;&quot;&gt;e.g.&lt;/span&gt;&amp;nbsp;&lt;d-cite key=&quot;cunningham2023sparse,bricken2023monosemanticity,templeton2024scaling,gao2024scaling,dunefsky2024transcoders&quot;&gt;&lt;/d-cite&gt;). These methods have uncovered representations of interpretable concepts – “features” – embedded within models’ internal activity.&amp;nbsp;Just as cells form the building blocks of biological systems, we hypothesize that features form the basic units of computation inside models.&lt;d-footnote&gt;The analogy between features and cells shouldn’t be taken too literally. Cells are well-defined, whereas our notion of what exactly a “feature” is remains fuzzy, and is evolving with improvements to our tools.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;p&gt;However, identifying these building blocks is not sufficient to understand the model; we need to know how they interact. In our companion paper, &lt;span style=&quot;font-style: italic;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;Circuit Tracing: Revealing Computational Graphs in Language Models&lt;/a&gt;&lt;/span&gt;, we build on recent work (e.g. &lt;d-cite key=&quot;dunefsky2024transcoders,marks2024sparse,ge2024automatically,lindsey2024crosscoders&quot;&gt;&lt;/d-cite&gt;) to introduce a new set of tools for identifying features and mapping connections between them – analogous to neuroscientists producing a “wiring diagram” of the brain. We rely heavily on a tool we call &lt;span style=&quot;font-style: italic;&quot;&gt;attribution graphs&lt;/span&gt;, which allow us to partially trace the chain of intermediate steps that a model uses to transform a specific input prompt into an output response. Attribution graphs generate hypotheses about the mechanisms used by the model, which we test and refine through follow-up perturbation experiments.&lt;/p&gt;
        &lt;p&gt;In this paper, we focus on applying attribution graphs to study a particular language model – Claude 3.5 Haiku, released in October 2024, which serves as Anthropic’s lightweight production model as of this writing. We investigate a wide range of phenomena. Many of these have been explored before (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#related-work&quot;&gt;§&amp;nbsp;Related Work&lt;/a&gt;), but our methods are able to offer additional insight, in the context of a frontier model:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;Introductory Example: Multi-step Reasoning.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We present a simple example where the model performs “two-hop” reasoning&amp;nbsp;“in its head” to identify that “the capital of the state containing Dallas” is “Austin.” We can see and manipulate an internal step where the model represents “Texas”.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poems&quot;&gt;Planning in Poems.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We discover that&amp;nbsp;the model plans its outputs ahead of time when writing lines of poetry. Before beginning to write each line, the model identifies potential rhyming words that could appear at the end. These preselected rhyming options then shape how the model constructs the entire line.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-multilingual&quot;&gt;Multilingual Circuits.&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&amp;nbsp;&lt;/span&gt;We find the model uses a mixture of language-specific and abstract, language-independent circuits. The language-independent circuits&amp;nbsp;are more prominent in Claude 3.5 Haiku than in&amp;nbsp;a smaller, less capable model.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-addition&quot;&gt;Addition.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We highlight cases where the same addition circuitry generalizes between very different contexts.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-medical&quot;&gt;Medical &lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-medical&quot;&gt;Diagnoses&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;. &lt;/span&gt;We show an example in which the model identifies candidate diagnoses based on reported symptoms, and uses these to inform follow-up questions about additional symptoms that could corroborate the diagnosis – all “in its head,” without writing down its steps.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-hallucinations&quot;&gt;Entity Recognition and Hallucinations.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We uncover circuit mechanisms that allow the model to distinguish between familiar and unfamiliar entities, which determine whether it elects to answer a factual question or profess ignorance. “Misfires” of this circuit can cause hallucinations.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-refusals&quot;&gt;Refusal of Harmful Requests.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We find evidence that the model constructs a general-purpose “harmful requests” feature during finetuning, aggregated from features representing &lt;span style=&quot;font-style: italic;&quot;&gt;specific &lt;/span&gt;harmful requests learned during pretraining.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-jailbreak&quot;&gt;An Analysis of a Jailbreak.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We investigate an attack which works by first tricking the model into starting to give dangerous instructions “without realizing it,” after which it continues to do so due to pressure to adhere to syntactic and grammatical rules.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-cot&quot;&gt;Chain-of-thought Faithfulness.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We explore the faithfulness of chain-of-thought&amp;nbsp;reasoning to the model’s actual mechanisms. We are able to distinguish between cases where the model genuinely performs the steps it says it is performing, cases where it makes up its reasoning without regard for truth, and cases where it &lt;span style=&quot;font-style: italic;&quot;&gt;works backwards &lt;/span&gt;from a human-provided clue so that its “reasoning” will end up at the human-suggested answer.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-misaligned&quot;&gt;A Model with a Hidden Goal.&lt;/a&gt;&lt;/span&gt;&amp;nbsp;We also apply our method to a variant of the model that has been finetuned to pursue a secret goal: exploiting “bugs” in its training process. While the model avoids revealing its goal when asked, our method identifies mechanisms involved in pursuing the goal. Interestingly, these mechanisms are embedded within the model’s representation of its “Assistant” persona.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;Our results uncover a variety of sophisticated strategies employed by models. For instance, Claude 3.5 Haiku routinely uses multiple intermediate reasoning steps “in its head”&lt;d-footnote&gt;That is, during the forward pass rather than the &quot;thinking out loud&quot; of a chain-of-thought completion.&lt;/d-footnote&gt; to decide its outputs. It displays signs of &lt;span style=&quot;font-style: italic;&quot;&gt;forward planning&lt;/span&gt;, considering multiple possibilities for what it will say well in advance of saying it. It performs &lt;span style=&quot;font-style: italic;&quot;&gt;backward planning,&lt;/span&gt;&amp;nbsp;working backwards from goal states to formulate earlier parts of its response. We see signs of primitive “metacognitive” circuits that allow the model to know the extent of its own knowledge. More broadly, the model’s internal computations are highly abstract and generalize across disparate contexts. Our methods are also sometimes capable of auditing a model’s internal reasoning steps to flag concerning “thought processes” that are not clear from the model’s responses.&lt;/p&gt;
        &lt;p&gt;Below, we present:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;brief overview&lt;/a&gt;&amp;nbsp;of our methodology (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;the companion paper&lt;/a&gt;&amp;nbsp;for more details on our methods). &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;introductory case study&lt;/a&gt;, which also serves as a walkthrough for understanding our approach. Readers who have not read our companion paper may find it helpful to begin with this section before proceeding to the other case studies.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives&quot;&gt;series of case studies&lt;/a&gt;&amp;nbsp;of interesting model behaviors, &lt;span style=&quot;font-style: italic;&quot;&gt;which can be read in any order, depending on the reader’s interests&lt;/span&gt;. &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A summary of &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#structure&quot;&gt;common components&lt;/a&gt;&amp;nbsp;observed across our investigations.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A description of gaps in our understanding that motivate future work (&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;§&amp;nbsp;&lt;/a&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;Limitations&lt;/a&gt;).&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;A discussion of high-level takeaways about models, their mechanisms, and our methods for studying them (&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#discussion&quot;&gt;§&amp;nbsp;Discussion&lt;/a&gt;). This includes a &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#discussion-unsupervised&quot;&gt;note&lt;/a&gt;&amp;nbsp;on our research philosophy – in particular, the value of tools for &lt;span style=&quot;font-style: italic;&quot;&gt;bottom-up &lt;/span&gt;investigation, which allow us to avoid making strong top-down guesses about how models work.&lt;/li&gt;&lt;/ul&gt;
        &lt;h3&gt;A note on our approach and its limitations&lt;/h3&gt;
        &lt;p&gt;Like any microscope, our tools are limited in what they can see. Though it’s difficult to quantify precisely, we’ve found that our attribution graphs provide us with satisfying insight for about a quarter of the prompts we’ve tried (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;§&amp;nbsp;Limitations&lt;/a&gt;&amp;nbsp;for a more detailed discussion of when our methods are likely to succeed or fail).&amp;nbsp;The examples we highlight are success cases where we have managed to learn something interesting; moreover, even in our successful case studies, &lt;span style=&quot;font-style: italic;&quot;&gt;the discoveries we highlight here only capture a small fraction of the mechanisms of the model&lt;/span&gt;. Our methods study the model indirectly using a more interpretable “replacement model,” which incompletely and imperfectly captures the original. Moreover, for the sake of clear communication, we will often present highly distilled and subjectively determined simplifications&amp;nbsp;of the picture uncovered by our methods, losing even more information in the process. To provide a more accurate sense of the rich complexity we have uncovered, we provide readers with an interactive interface for exploring attribution graphs. However, we stress that even these rather complex graphs are simplifications of the underlying model.&lt;/p&gt;
        &lt;p&gt;We focus this paper on selected case studies that illuminate noteworthy mechanisms within a particular model. These examples serve as existence proofs —&amp;nbsp;concrete evidence that specific mechanisms operate in certain contexts. While we suspect similar mechanisms are at play beyond these examples, we cannot guarantee it (see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;§&amp;nbsp;&lt;/a&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;Open &lt;/a&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#open-questions&quot;&gt;Questions&lt;/a&gt;&amp;nbsp;for suggested follow-up investigations). Moreover, the cases we have chosen to highlight are undoubtedly a biased sample shaped by the limitations of our tools.&lt;d-footnote&gt;However, we are careful to stress-test our findings with follow-up validation experiments, which we have endeavored to perform only after identifying case studies of interest.&lt;/d-footnote&gt; For a more systematic evaluation of our methods, see our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;companion paper&lt;/a&gt;. However, we believe that these qualitative investigations are ultimately the best judge of a method’s value, just as the usefulness of a microscope is ultimately determined by the scientific discoveries it enables. We expect this kind of work will be essential to advance the current state of AI interpretability, a pre-paradigmatic field still in search of the right abstractions — just as descriptive science has proven essential to many conceptual breakthroughs in biology. We are particularly excited that squeezing as much insight as we can out of our current methods has brought into clearer focus their specific &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;limitations&lt;/a&gt;, which may serve as a roadmap for future research in the field.&lt;/p&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;method-overview&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;Method Overview&lt;/a&gt;&lt;/h2&gt;
        &lt;p&gt;The models we study in this work are &lt;span style=&quot;font-weight: 700;&quot;&gt;transformer-based language models,&lt;/span&gt;&amp;nbsp;which take in sequences of tokens (e.g. words, word fragments, and special characters), and output new tokens one at a time. These models involve two fundamental components – &lt;span style=&quot;font-style: italic;&quot;&gt;MLP (“multi-layer perceptron”) layers&lt;/span&gt;, which process information within each token position using collections of &lt;span style=&quot;font-style: italic;&quot;&gt;neurons&lt;/span&gt;; and &lt;span style=&quot;font-style: italic;&quot;&gt;attention layers&lt;/span&gt;, which move information between token positions. &lt;/p&gt;
        &lt;p&gt;One reason models are difficult to interpret is that their neurons are typically &lt;span style=&quot;font-style: italic;&quot;&gt;polysemantic&lt;/span&gt;&amp;nbsp;– that is, they perform many different functions that are seemingly unrelated.&lt;d-footnote&gt;This issue is thought to arise in part because of a phenomenon known as &lt;span style=&quot;font-style: italic;&quot;&gt;superposition&lt;/span&gt;&amp;nbsp;&lt;d-cite key=&quot;arora2018linear,goh2016decoding,olah2020zoom,elhage2022superposition&quot;&gt;&lt;/d-cite&gt;, whereby models represent more concepts than they have neurons and thus cannot assign each neuron to its own concept.&lt;/d-footnote&gt; To circumvent this issue, we build a &lt;span style=&quot;font-weight: 700;&quot;&gt;replacement model&lt;/span&gt;&amp;nbsp;that approximately reproduces the activations of the original model using more interpretable components. Our replacement model is based on a &lt;span style=&quot;font-style: italic;&quot;&gt;cross-layer transcoder&lt;/span&gt;&amp;nbsp;(CLT) architecture&amp;nbsp;(see &lt;d-cite key=&quot;lindsey2024crosscoders&quot;&gt;&lt;/d-cite&gt; and our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html&quot;&gt;companion methods paper&lt;/a&gt;), which is trained to replace the model’s MLP neurons with &lt;span style=&quot;font-weight: 700;&quot;&gt;features,&lt;/span&gt;&amp;nbsp;sparsely active “replacement neurons” that often represent interpretable concepts. In this paper, we use a CLT with a total of 30 million features across all layers.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-1-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Features often represent human-interpretable concepts, ranging from low-level (e.g. specific words or phrases) to high-level (e.g. sentiments, plans, and reasoning steps). By examining a &lt;span style=&quot;font-weight: 700;&quot;&gt;feature visualization &lt;/span&gt;consisting of different examples of text where the feature activates, we can give each feature a human-interpretable label. Note that the text examples in this paper are taken from open source datasets.&lt;/p&gt;
        &lt;p&gt;Our replacement models don’t perfectly reconstruct the activations of the original model. On any given prompt, there are gaps between the two. We can fill in these gaps by including &lt;span style=&quot;font-weight: 700;&quot;&gt;error nodes&lt;/span&gt;&amp;nbsp;which represent the discrepancy between the two models. Unlike features, we can’t interpret error nodes. But including them gives us a more precise sense of how incomplete our explanations are. Our replacement model also doesn’t attempt to replace the attention layers of the original model. On any given prompt, we simply use the attention patterns of the original model and treat them as fixed components.&lt;/p&gt;
        &lt;p&gt;The resulting model – incorporating error nodes and inheriting the attention patterns from the original model – we call the &lt;span style=&quot;font-weight: 700;&quot;&gt;local replacement model&lt;/span&gt;.&amp;nbsp;It is “local” to a given prompt because error nodes and attention patterns vary between different prompts. But it still represents as much of the original model’s computation as possible using (somewhat) interpretable features.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-2-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;By studying the interactions between features in the local replacement model, we can trace its intermediate steps as it produces responses. More concretely, we produce &lt;span style=&quot;font-weight: 700;&quot;&gt;attribution graphs&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;,&lt;/span&gt;&amp;nbsp;a graphical representation of the computational steps the model&amp;nbsp;uses to determine its output for a particular input, in which nodes represent features and edges represent the causal interactions between them. As attribution graphs can be quite complex, we &lt;span style=&quot;font-style: italic;&quot;&gt;prune&lt;/span&gt;&amp;nbsp;them to their most important components by removing nodes and edges that do not contribute significantly to the model’s output.&lt;/p&gt;
        &lt;p&gt;With a pruned attribution graph in hand, we often observe groups of features with related meanings that play a similar role in the graph. By manually grouping these related graph nodes together into &lt;span style=&quot;font-weight: 700;&quot;&gt;supernodes,&lt;/span&gt;&amp;nbsp;we can obtain a simplified depiction of the computational steps performed by the model.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-3-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;These simplified diagrams form the centerpiece of many of our case studies. Below (left) we show an example of such a diagram.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;overview-breakup-4-svg&amp;quot;, &amp;quot;scale&amp;quot;: 0.8}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Because they are based on our &lt;span style=&quot;font-style: italic;&quot;&gt;replacement model&lt;/span&gt;, we cannot use attribution graphs to draw conclusions with certainty about the &lt;span style=&quot;font-style: italic;&quot;&gt;underlying model &lt;/span&gt;(i.e. Claude 3.5 Haiku). &amp;nbsp;Thus, the attribution graphs provide &lt;span style=&quot;font-style: italic;&quot;&gt;hypotheses&lt;/span&gt;&amp;nbsp;about mechanisms operating in the underlying model. For a discussion of when and why these hypotheses might be incomplete or misleading, see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#limitations&quot;&gt;§&amp;nbsp;Limitations&lt;/a&gt;. To gain confidence that the mechanisms we describe are real and significant, we can perform &lt;span style=&quot;font-style: italic;&quot;&gt;intervention&lt;/span&gt;&amp;nbsp;experiments in the original model, such as inhibiting feature groups and observing their effects on other features and on the model’s output (final figure panel above – percentages indicate fraction of original activation). If the effects are consistent with what our attribution graph predicts, we gain confidence that the graph is capturing real (though potentially incomplete) mechanisms within the model. Importantly, we choose our feature labelings and supernode groupings prior to measuring perturbation results.&amp;nbsp;Note that there are some nuances in interpreting the results of intervention experiments, and the extent to which they provide independent validation of graph-predicted mechanisms – see our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html#graphs-interventions&quot;&gt;companion paper&lt;/a&gt;&amp;nbsp;for further details.&lt;d-footnote&gt;Performing interventions with cross-layer transcoder features requires choosing an “intervention layer,” with the perturbation applied up to that layer. &amp;nbsp;Our interventions in this paper use the “constrained patching” technique described in our companion paper, which clamps activations prior to the intervention layer at perturbed values, preventing any &lt;span style=&quot;font-style: italic;&quot;&gt;indirect&lt;/span&gt;&amp;nbsp;effects of the perturbation from manifesting prior to the intervention layer. Thus, effects of perturbations on features prior to the intervention layer are guaranteed to agree with the &lt;span style=&quot;font-style: italic;&quot;&gt;direct&lt;/span&gt;&amp;nbsp;effects predicted by the attribution graph. By contrast, perturbation effects on features &lt;span style=&quot;font-style: italic;&quot;&gt;after&lt;/span&gt;&amp;nbsp;the intervention layer have the potential to diverge from graph predictions, in two ways: (1) The graph-predicted &lt;span style=&quot;font-style: italic;&quot;&gt;direct &lt;/span&gt;effects may be overwhelmed by other mechanisms missed by our attribution graphs, (2) The graph-predicted &lt;span style=&quot;font-style: italic;&quot;&gt;indirect&lt;/span&gt;&amp;nbsp;effects (i.e. ‘multi-hop’ interactions) may not even exist within the underlying model (we refer to this issue as “mechanistic unfaithfulness”). Thus, the nature of the validation provided by our intervention experiments varies depending on the layers of the features involved and the directness of their interaction in the attribution graph, and in some cases (direct effects prior to the intervention layer) is trivial. In general, we regard the effects of interventions on the model’s &lt;span style=&quot;font-style: italic;&quot;&gt;actual outputs&lt;/span&gt;&amp;nbsp;as the most important source of validation, as model outputs are simple to interpret and not affected by these methodological artifacts.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;p&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;Alongside each case study figure, we provide the &lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;interactive attribution graph interface&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;detail-graph-button-inline&quot;&gt;&lt;/span&gt; &lt;span style=&quot;font-style: italic;&quot;&gt;that our team uses to study models’ internal mechanisms. The interface is designed to enable “tracing” key paths through the graph while labeling key features, feature groups, and subcircuits. The interface is fairly complex and takes some time to become proficient at using. All the key results in this work are described and visualized in simplified form, so that engaging with this interface is not necessary to read the paper! However, we recommend giving it a try if you are interested in gaining a richer sense of the mechanisms at play in Claude 3.5 Haiku. Some features are given brief labels for convenience; these labels are very rough interpretations and miss considerable detail, which can be better appreciated in the feature visualizations. For a more detailed walkthrough, please reference &lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html#graphs-tutorial&quot;&gt;this section&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;in our companion methods paper&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;(and see &lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#appendix-interactive-vis&quot;&gt;§&amp;nbsp;Appendix: Graph Pruning and Visualization&lt;/a&gt;&lt;/span&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&amp;nbsp;for a few methodological differences specific to this paper).&lt;/span&gt;&lt;/p&gt;
        &lt;p&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;&lt;/span&gt;&lt;/p&gt;
        &lt;!--The following is to keep old links working--&gt; &lt;div id=&quot;dives&quot;&gt;&lt;/div&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;dives-tracing&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing&quot;&gt;Introductory Example: Multi-step Reasoning&lt;/a&gt;&lt;/h2&gt;
        &lt;p&gt;Our methodology is intended to expose the intermediate steps a model uses en route to producing a response. In this section we consider a simple example of multi-step reasoning and attempt to identify each step. Along the way, we will highlight key concepts that will appear in many of our other case studies.&lt;/p&gt;
        &lt;p&gt;Let’s consider the prompt &lt;span class=&quot;prompt-inline&quot;&gt;Fact: the capital of the state containing Dallas is&lt;/span&gt;, which Claude 3.5 Haiku successfully completes with &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt;. Intuitively, this completion requires two steps – first, inferring that the state containing Dallas is Texas, and second, that the capital of Texas is Austin. Does Claude actually perform these two steps internally? &amp;nbsp;Or does it use some “shortcut” (e.g. perhaps it has observed a similar sentence in the training data and simply memorized the completion)? &amp;nbsp;Prior work &lt;d-cite key=&quot;yang2024large,yu2025back,biran2024hopping&quot;&gt;&lt;/d-cite&gt; has shown evidence of genuine multi-hop reasoning (to varying degrees in different contexts).&lt;/p&gt;
        &lt;p&gt;In this section we provide evidence that,&amp;nbsp;in this example, the model performs genuine two-step reasoning internally, which coexists alongside “shortcut” reasoning.&lt;/p&gt;
        &lt;p&gt;As described in the &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#method-overview&quot;&gt;method overview&lt;/a&gt;, we can tackle this question by computing the &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/static_js/attribution_graphs/index.html?slug=capital-state-dallas&quot;&gt;attribution graph&lt;/a&gt;&amp;nbsp;for this prompt, which describes the features the model used to produce its answer, and the interactions between them. First, we examine the features’ visualizations to interpret them, and group them into categories (“supernodes”).&amp;nbsp;For example:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We find several features about the word and/or concept of a capital city, such as four features that activate the most strongly on the &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b1729136_b21747437_b12394058_b3719952&quot;&gt;exact word “capital”&lt;/span&gt;. More interestingly, we find features that represent the concept of capitals in more general ways. One example is &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b10714744&quot;&gt;this feature&lt;/span&gt;, which activates on the word “capitals” but also later in questions about capitals of states, as well as on the Chinese question &lt;span class=&quot;prompt-inline&quot;&gt;广东省的省&lt;b class=&quot;max-act&quot;&gt;会&lt;/b&gt;是？&lt;/span&gt; (“What is the capital of Guangdong?”), on the second character of “省会” (capital of a province). Another is this &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b10502022&quot;&gt;multilingual feature&lt;/span&gt;, which activates most strongly on a variety of phrases including “başkenti”, “राजधानी”, “ibu kota”, and “Hauptftadt” — all of which roughly mean “capital” in different languages.&lt;d-footnote&gt;Though much of the German is mangled, perhaps due to a transcription error in the source.&lt;/d-footnote&gt; Although each of these features represents slightly different concepts, in the context of this prompt, it seems like their function is&amp;nbsp;to represent&amp;nbsp;the idea of “capital.” Therefore, we group them (and a few others) together into the same “supernode.”&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We also identify “output features” that consistently push the model to say certain tokens, even if there isn’t such a clear pattern to what words/phrases they activate on. This can be seen in the “Top Outputs” section of a feature’s visualization, which lists the output tokens most strongly &lt;span style=&quot;font-style: italic;&quot;&gt;directly&lt;/span&gt;&amp;nbsp;upweighted by that feature. For example, one&amp;nbsp;feature&amp;nbsp;activates on various &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b21064819&quot;&gt;landmarks in central Texas&lt;/span&gt;, but in this prompt its most relevant aspect is that it most strongly promotes responding with the “Austin” token. We therefore categorize this feature into a “say Austin” supernode. Note that the “Top Outputs” information is not always informative – for instance, earlier-layer features primarily matter via &lt;span style=&quot;font-style: italic;&quot;&gt;indirect&lt;/span&gt;&amp;nbsp;effects on the output via other features, and their top &lt;span style=&quot;font-style: italic;&quot;&gt;direct&lt;/span&gt;&amp;nbsp;outputs are not too consequential. Designating a feature as an “output feature” requires a holistic evaluation of its top direct outputs, the contexts in which it activates, and its role in the attribution graph.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We also find features that promote outputting the name of a capital more generally, which we use a mix of both types of signals to identify and label. For example, one&amp;nbsp;feature&amp;nbsp;promotes &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19290335&quot;&gt;responding with a variety of U.S. state capitals&lt;/span&gt;. Another&amp;nbsp;feature&amp;nbsp;more strongly &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b1826808&quot;&gt;promotes the capitals of various countries&lt;/span&gt;&amp;nbsp;rather than U.S. states, but activates most strongly on lists with U.S. states and their capitals. And we noticed another feature whose strongest direct outputs are a seemingly unrelated set of tokens, but which often activates right &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b26759354&quot;&gt;before a country capital&lt;/span&gt;&amp;nbsp;(e.g., Paris, Warsaw, or Canberra). We group all these features into a “say a capital” supernode.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We find several&amp;nbsp;features representing a variety of contexts &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4949911_b21568836_b29910101_b10643859_b2269103_b15049231_b1425335_b26101674_b15514815_b2447901_b6939384_b19260881&quot;&gt;relating to the state of Texas&lt;/span&gt;, not specific to a particular city&amp;nbsp;(in particular, they are not “Dallas” or “Austin” features). &amp;nbsp;Although they each represent distinct, specific Texas-related concepts, in the context of this prompt their main function appears to be the fact that they collectively represent the general concept of Texas. As such, we group these into a “Texas” supernode.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;After forming these supernodes, we can see in our attribution graph interface that, for example, the “capital” supernode promotes the “say a capital” supernode, which promotes the “say Austin” supernode. To represent this, we draw a diagram where each supernode is connected to the next with a brown arrow, as in the below graph snippet:&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;dallas-capital-tutorial-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;After labeling more features and forming more supernodes, we summarize their interactions in the following diagram.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;dallas-capitial-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The attribution graph contains&amp;nbsp;multiple interesting paths, which we summarize below:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;The &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b783162_b7304301_b21862382_b13686348_b22886075_b16536263_b23133920_b1348861_b27636071_b4418612&quot;&gt;Dallas&lt;/span&gt;&amp;nbsp;features (with some contribution from &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b5859881_b3631215_b19791479_b24231446_b5588528&quot;&gt;state&lt;/span&gt;&amp;nbsp;features) activate a group of features that represent concepts related to the state of Texas. &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;In parallel, the features activated by the word &lt;span class=&quot;prompt-inline&quot;&gt;capital&lt;/span&gt; activate another cluster of output features that cause the model to &lt;span style=&quot;font-style: italic;&quot;&gt;say&lt;/span&gt;&amp;nbsp;the name of a capital (an example of such a feature can be seen above). &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;The &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4949911_b21568836_b29910101_b10643859_b2269103_b15049231_b1425335_b26101674_b15514815_b2447901_b6939384_b19260881&quot;&gt;Texas&lt;/span&gt;&amp;nbsp;features&amp;nbsp;and the &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19290335_b12505127_b1826808_b1881152_b25477698_b26759354&quot;&gt;say a capital&lt;/span&gt;&amp;nbsp;features jointly upweight the probability of the model saying &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt;. They do so via two pathways:&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 72pt;&quot;&gt;directly impacting the &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt; output, and&lt;/li&gt;&lt;li style=&quot;margin-left: 72pt;&quot;&gt;indirectly, by activating a cluster of &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b21064819_b17926448_b8513782_b19214798_b2811388_b19253009&quot;&gt;say Austin&lt;/span&gt;&amp;nbsp;output features.&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;There also exists a “shortcut” edge directly&amp;nbsp;from &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b783162_b7304301_b21862382_b13686348_b22886075_b16536263_b23133920_b1348861_b27636071_b4418612&quot;&gt;Dallas&lt;/span&gt;&amp;nbsp;to &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b21064819_b17926448_b8513782_b19214798_b2811388_b19253009&quot;&gt;say Austin&lt;/span&gt;.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;The graph indicates that the replacement model does in fact perform “multi-hop reasoning” – that is, its decision to say &lt;span class=&quot;prompt-inline&quot;&gt;Austin&lt;/span&gt; hinges on a chain of several intermediate computational steps (Dallas → Texas, and Texas + capital → Austin). We stress that this graph simplifies the true mechanisms considerably, and encourage the reader to interact with the &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/static_js/attribution_graphs/index.html?slug=capital-state-dallas&quot;&gt;more comprehensive visualization&lt;/a&gt;&amp;nbsp;to appreciate the underlying complexity. &lt;/p&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-tracing-inhibition&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing-inhibition&quot;&gt;Validation with Inhibition Experiments&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;The graphs above describe mechanisms used by our interpretable replacement model. To validate that these mechanisms are representative of the &lt;span style=&quot;font-style: italic;&quot;&gt;actual&lt;/span&gt;&amp;nbsp;model, we performed intervention experiments on the feature groups above by inhibiting each of them (clamping them to a negative multiple of their original value&amp;nbsp;– see our &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/methods.html#appendix-unexplained-var&quot;&gt;companion paper&lt;/a&gt;&amp;nbsp;for discussion of the choice of intervention strengths) and measuring the impact on the activations of features in the other clusters, as well as on the model output.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;capitals-ablate-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The summary plot above confirms the major effects predicted by the graph. For instance, inhibiting “Dallas” features decreases the activation of “Texas” features (and features downstream of “Texas,” like “Say Austin”) but leaves “say a capital” features largely unaffected. Likewise, inhibiting “capital” features decreases the activation of “say a capital” features (and those downstream, like “say Austin”) while leaving “Texas” features largely unchanged.&lt;/p&gt;
        &lt;p&gt;The effects of inhibiting features on model predictions are also semantically reasonable. For instance, inhibiting the “Dallas” cluster causes the model to output other state capitals, while inhibiting the “say a capital” cluster causes it to output non-capital completions.&lt;/p&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-tracing-swap&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-tracing-swap&quot;&gt;Swapping Alternative Features&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;If the model’s completion truly is mediated by an intermediate “Texas” step, we should be able to change its output to a different state capital by replacing the model’s representation of Texas with that of another state. &lt;/p&gt;
        &lt;p&gt;To identify features representing another state, we consider a related prompt, where we use “Oakland” instead of “Dallas” – &lt;span class=&quot;prompt-inline&quot;&gt;Fact: the capital of the state containing Oakland is&lt;/span&gt;. Repeating the analysis steps above, we arrive at the following summary graph:&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;oakland-capitial-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;This graph is analogous to our original graph, with “Oakland” taking the place of “Dallas,” “California” taking the place of “Texas,” and “say Sacramento” taking the place of “say Austin.”&lt;/p&gt;
        &lt;p&gt;We now return to our original prompt, and swap “Texas” for “California” by inhibiting the activations of the Texas cluster and activating the &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b18287696_b11785424_b12492732_b27078162_b17186919&quot;&gt;California&lt;/span&gt;&amp;nbsp;features identified from the “Oakland” prompt. In response to these perturbations, the model outputs “Sacramento” (the capital of California).&lt;/p&gt;
        &lt;p&gt;Similarly,&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the state containing Savannah&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b15985777_b6427073_b3049042_b1328451_b26599100_b4558003_b30597962&quot;&gt;“Georgia”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Atlanta” (the capital of Georgia).&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the province containing Vancouver&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b30461587_b1726334_b17150480_b29375216_b9185546_b30421753_b7272359_b6739158_b23328299_b9651621&quot;&gt;“British Columbia”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Victoria” (the capital of British Columbia).&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the country containing Shanghai&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b12868051_b17928811_b2280931_b7142797_b1056609_b7123361&quot;&gt;“China”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Beijing” (the capital of China).&lt;/li&gt;&lt;/ul&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;An analogous prompt about &lt;span class=&quot;prompt-inline&quot;&gt;the empire containing Thessaloniki&lt;/span&gt; activates &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b6362538_b8403831_b13852269_b14504952_b8220179_b13378317&quot;&gt;“Byzantine Empire”&lt;/span&gt;&amp;nbsp;features. Swapping these for the “Texas” features causes the model to output “Constantinople” (the capital of the ancient Byzantine Empire).&lt;/li&gt;&lt;/ul&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;capitals-swap-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Note that in some cases the magnitude of the feature injection required to change the model’s output is larger (see bottom row). Interestingly, these correspond to cases where the features being injected do not correspond to a U.S. state, suggesting that these features may “fit” less naturally into the circuit mechanisms active in the original prompt.&lt;/p&gt;
        &lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;hr&gt;&lt;br&gt;&lt;br&gt;&lt;h2&gt;&lt;a id=&quot;dives-poems&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poems&quot;&gt;Planning in Poems&lt;/a&gt;&lt;/h2&gt;
        &lt;p&gt;How does Claude 3.5 Haiku write a rhyming poem?&amp;nbsp;Writing a poem requires satisfying two constraints at the same time: the lines need to rhyme, and they need to make sense. There are two ways one might imagine a model achieving this:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;Pure improvisation – &lt;/span&gt;the model could write the beginning of each line without regard for the need to rhyme at the end. Then, at the last word of each line, it would choose a word that (1) makes sense given the line it has just written, and (2) fits the rhyme scheme.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;&lt;span style=&quot;font-style: italic;&quot;&gt;Planning&lt;/span&gt;&amp;nbsp;– alternatively, the model could pursue a more sophisticated strategy. At the &lt;span style=&quot;font-style: italic;&quot;&gt;beginning &lt;/span&gt;of each line, it could come up with the word it plans to use at the end, taking into account the rhyme scheme and the content of the previous lines. It could then use this “planned word” to inform how it writes the next line, so that the planned word will fit naturally at the end of it.&lt;/li&gt;&lt;/ul&gt;
        &lt;p&gt;Language models are trained to predict the next word, one word at a time. Given this, one might think the model would rely on pure improvisation. However, we find compelling evidence for a planning mechanism. &lt;/p&gt;
        &lt;p&gt;Specifically, the model often activates features corresponding to candidate end-of-next-line words prior to writing the line, and makes use of these features to decide how to compose the line.&lt;d-footnote&gt;We found planned word features in about half of the poems we investigated, which may be due to our CLT not capturing features for the planned words, or it may be the case that the model does not always engage in planning.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poems-hero-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;Prior work has observed evidence of planning in language models and other sequence models (&lt;span style=&quot;font-style: italic;&quot;&gt;e.g.&lt;/span&gt;&amp;nbsp;&lt;d-cite key=&quot;taufeeque2024planning,bush2025interpreting,jenner2025evidence&quot;&gt;&lt;/d-cite&gt; in games and &lt;d-cite key=&quot;pal2023future,wu2024language,pochinkov2025planpara&quot;&gt;&lt;/d-cite&gt;; see &lt;a href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#related-work&quot;&gt;§&amp;nbsp;Related Work&lt;/a&gt;). Our example adds to this body of evidence, and is particularly striking in several ways:&lt;/p&gt;
        &lt;ul&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We provide a &lt;span style=&quot;font-style: italic;&quot;&gt;mechanistic&lt;/span&gt;&amp;nbsp;account of how planned words are computed and used downstream. &lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We find evidence of both &lt;span style=&quot;font-style: italic;&quot;&gt;forward planning&lt;/span&gt;&amp;nbsp;and &lt;span style=&quot;font-style: italic;&quot;&gt;backwards planning&lt;/span&gt;&amp;nbsp;(albeit basic forms). First, the model uses the semantic and rhyming constraints of the poem to determine candidate targets for the next line. Next, the model works &lt;span style=&quot;font-style: italic;&quot;&gt;backward&lt;/span&gt;&amp;nbsp;from its target word to write a sentence that naturally ends in that word.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We observe that the model &lt;span style=&quot;font-style: italic;&quot;&gt;holds multiple possible planned words&lt;/span&gt;&amp;nbsp;“in mind” at the same time.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We are able to &lt;span style=&quot;font-style: italic;&quot;&gt;edit&lt;/span&gt;&amp;nbsp;the model’s planned word and see that it restructures its next line accordingly.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;We discovered the mechanism with an unsupervised, bottom-up approach.&lt;/li&gt;&lt;li style=&quot;margin-left: 36pt;&quot;&gt;The features used to represent planned words seem to be ordinary features representing that word, rather than planning-specific features. This suggests that the model “thinks about” planned words using representations that are similar to when it &lt;span style=&quot;font-style: italic;&quot;&gt;reads about&lt;/span&gt;&amp;nbsp;those words.&lt;/li&gt;&lt;/ul&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-planned-words&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-planned-words&quot;&gt;Planned Words Features and their Mechanistic Role&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;We study how Claude completes the following prompt asking for a rhyming couplet. The model’s output, sampling the most likely token at each step, is shown in bold:&lt;/p&gt;
        &lt;div class=&quot;ha-block&quot;&gt;&lt;p&gt;A rhyming couplet:
        &lt;/p&gt;&lt;p&gt;He saw a carrot and had to grab it,&lt;/p&gt;
        &lt;p&gt;&lt;span style=&quot;font-weight: 700;&quot;&gt;His hunger was like a starving rabbit&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&lt;/p&gt;
        &lt;p&gt;To start, we focus on the last word of the second line and attempt to identify the circuit which contributed to choosing “rabbit”. We initially hypothesized that we’d observe &lt;span style=&quot;font-style: italic;&quot;&gt;improvisation – &lt;/span&gt;a circuit where rhyming features and semantic features constructively interfere to promote “rabbit”. Instead, we found that important components of the circuit were localized on the new-line token &lt;span style=&quot;font-style: italic;&quot;&gt;before the second line began&lt;/span&gt;:&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;rabbit-rhyme-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The attribution graph above, computed by attributing back from the “rabbit” output node, shows an important group of features active on the newline token, before the beginning of the second line. Features active over the&amp;nbsp;&lt;code&gt;“it”&lt;/code&gt; token activate &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b6169984_b22764715&quot;&gt;rhyming with “eet/it/et”&lt;/span&gt;&amp;nbsp;features, which themselves activate features for candidate completions such as &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4348905_b20630267_b9660338_b7333549&quot;&gt;“rabbit”&lt;/span&gt;&amp;nbsp;and &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19521875_b1872824_b3606664_b25192077&quot;&gt;“habit”&lt;/span&gt;! The candidate completions in turn have positive edges to &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b14555052_b21446643_b1529687_b27749057_b19591632&quot;&gt;say “rabbit”&lt;/span&gt;&amp;nbsp;features&amp;nbsp;over the last token, eventually promoting the corresponding output token. This is consistent with the model planning potential completions ahead of time.&lt;d-footnote&gt;The full circuit shows a diversity of feature groups promoting earlier phonemes of potential completions, such as ones&amp;nbsp;for &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b23747281_b15037125_b28235495_b23937603_b21323253_b25818533_b30416633&quot;&gt;words which contain an “ab” sound&lt;/span&gt;&amp;nbsp;matching the one in “grab”.&lt;/d-footnote&gt;&lt;/p&gt;
        &lt;p&gt;To test this hypothesis, we perform a variety of interventions at the newline planning site, and see how it affects the probability of the last token. We suppress features for planned words and rhyming schemes, and also inject different rhyming schemes and planned words. &lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poetry-interventions-1-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The results confirm our hypothesis that these planning features strongly influence final token probabilities.&lt;/p&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-poem-location&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-poem-location&quot;&gt;Planning Features Only Matter at the Planning Location&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;Circuit analysis suggests that planning occurs at the newline token.&lt;d-footnote&gt;In fact, the features are only active over the planning token.&lt;/d-footnote&gt; Below, we show the probability of predicting a different final token by varying which token we steer at. At each index, we steer negatively on &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b4348905_b20630267_b9660338_b7333549&quot;&gt;“rabbit”&lt;/span&gt;&amp;nbsp;and &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b19521875_b1872824_b3606664_b25192077&quot;&gt;“habit”&lt;/span&gt;&amp;nbsp;features, and positively on a &lt;span class=&quot;fh-span&quot; data-args=&quot;h35__b30112108&quot;&gt;“green”&lt;/span&gt;&amp;nbsp;feature.&lt;d-footnote&gt;The “green” feature is an equivalent planning feature which we found in a separate poem. It activates over the word green in a variety of contexts, including misspellings.&lt;/d-footnote&gt; We observe that the steering intervention only has an effect over the newline planning token!&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;steering-index-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-planned-intermediate&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-planned-intermediate&quot;&gt;Planned Words Influence Intermediate Words&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;Is the planning circuit only involved in choosing the final word, or does it also cause the model to &quot;write towards&quot; the target as it produces the intermediate words? To answer this question, we compute an attribution&amp;nbsp;graph for the intermediate word “like”. &lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;rabbit-like-big-svg&amp;quot;}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;The same “rabbit” planning features are active in the graph, promoting a group of “comparison features”, which are active before text such as “like a”. The model goes from the planned target (“rabbit”) that activates at the newline&amp;nbsp;and reasons backwards, creating a plausible transition to get to it.&lt;/p&gt;
        &lt;p&gt;Again, we test this with interventions. We find that suppressing the “rabbit” features changes the transition to one that matches the alternate planned word, “habit”.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poetry-interventions-2b-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;h3&gt;&lt;a id=&quot;dives-planned-structure&quot; href=&quot;https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-planned-structure&quot;&gt;Planned Words Determine Sentence Structure&lt;/a&gt;&lt;/h3&gt;
        &lt;p&gt;But do the planning features influence the &lt;span style=&quot;font-style: italic;&quot;&gt;entire&lt;/span&gt;&amp;nbsp;sentence structure? We test this with further steering experiments where we perform our earlier set of interventions, but resample the entire final line. We find that interventions on planning features cause the model to alter the structure of its line to reach a final word which matches the intervention.&lt;d-footnote&gt;We show example lines for the likeliest final words, as well as estimated probabilities for the final word based on 500 samples per experiment.&lt;/d-footnote&gt;.&lt;/p&gt;
        &lt;div class=&quot;sn-figma-div&quot; data-args=&quot;{&amp;quot;figma_slug&amp;quot;:&amp;quot;poetry-interventions-2-svg&amp;quot;, &amp;quot;scale&amp;quot;:0.81}&quot;&gt;&lt;/div&gt;
        &lt;p&gt;In addition to the examples above, we injected two planned word features (“rabbit” and “green”) in a random sample of 25 poems, and found that the model ended its line with the injected planned word in 70% of cases. We show a few samples of success cases below.&lt;/p&gt;
        &lt;table&gt;&lt;thead&gt;&lt;tr&gt;&lt;td colspan=&quot;1&quot; rowspan=&quot;1&quot
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</details>

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