# New ARENA material: 8 exercise sets on alignment science & interpretability

TLDR
====

This is a post announcing a lot of new ARENA material I've been working on for a while, which is now available for study [here](https://learn.arena.education/) (currently on the `alignment-science` branch, but planned to be merged into main this Sunday).

There's a set of exercises (each one contains about 1-2 days of material) on the following topics:

*   **Linear Probes** (replication of the "Geometry of Truth" paper, plus Apollo's "Probing for Deception" work)
*   **Activation Oracles** (based around [this demo notebook](https://colab.research.google.com/drive/1wMiy9b1ZHXQCGuodEB8n1oW-DSTvCqWI), with additional exercises on model diffing)
*   **Attribution graphs** (you can build them from scratch here including all the graph pruning implementations, and also use the `circuit-tracer` library)
*   **Emergent Misalignment** (mostly based on [Soligo](https://arxiv.org/abs/2506.11613) [&](https://arxiv.org/abs/2506.11618) [Turner's](https://arxiv.org/abs/2602.07852) work; this also covers a lot of "basics of how to work with model organisms" like writing autoraters, using LoRA finetunes, etc)
*   **Science of Misalignment** (walkthrough of 2 case studies: Palisade's "[Shutdown Resistance](https://palisaderesearch.org/blog/shutdown-resistance)" & GDM's [follow-up](https://www.alignmentforum.org/posts/wnzkjSmrgWZaBa2aC/self-preservation-or-instruction-ambiguity-examining-the), and [Alignment Faking](https://www.anthropic.com/research/alignment-faking))
*   **Reasoning Model Interpretability** (guided replication of Thought Anchors plus the blackmail extension)
*   **LLM Psychology & Persona Vectors** (replicates the "[assistant axis](https://www.anthropic.com/research/assistant-axis)" paper including activation capping technique, and also has you create a [persona vector](https://www.anthropic.com/research/persona-vectors) extraction pipeline)
*   **Investigator Agents** (basically takes you through building mini-[Petri](https://alignment.anthropic.com/2025/petri/) from scratch, including the additional eval-awareness from Petri 2.0)

* * *

New material
============

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/82ee26806c6ba0c2ccc224368126c56647827148279ebbb2.png)

Most of this material is going into the new "Alignment Science" chapter (this framing is borrowed from Anthropic's [alignment science blog](https://alignment.anthropic.com/); 3/5 of this chapter's exercises directly pull from material in this blog).

Linear probes & activation oracles are both in the interpretability chapter (in section 1.3: "Probing & Representations"). We split the SAE chapter in half: the main content is now in section 1.3, and the circuit-based exercises are in section 1.4: "Circuits" (which is where the attribution graph exercises are). The other 5 pieces of content are separate days in the new chapter 4: Alignment Science.

We recommend (1.1) Transformers from Scratch as a prerequisite to all of these. For some of them, (1.2) Intro to Mech Interp will also be very useful groundwork. There are no other dependencies, so you can jump in at any one of the following exercises. This includes chapter 4: although they are in the order we'll be using for ARENA (and some early chapters do introduce ideas which are expanded on in later chapters), there are no strict dependencies between anything in this chapter.

(1.3.1) Linear Probes
---------------------

In these exercises, you'll replicate 2 key probing papers:

*   [The Geometry of Truth](https://arxiv.org/abs/2310.06824) (Marks, Tegmark) which visualizes clean linear structure in LLM activations on true/false statements
*   [Detecting Strategic Deception Using Linear Probes](https://arxiv.org/abs/2502.03407) (Apollo Research) which has a similar training method but tests generalization to realistic deceptive scenarios like concealing insider trading

After this, you'll explore some additional probing architectures (e.g. attention probes), following [EleutherAI's implementation](https://blog.eleuther.ai/attention-probes/).

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/a82c5deaedd5b7f32d1d41bb9b3edc19999672c367f39e18.png)

(1.3.4) Activation Oracles
--------------------------

These exercises are closely based on the [demo notebook](https://colab.research.google.com/drive/1wMiy9b1ZHXQCGuodEB8n1oW-DSTvCqWI) that was published along with the activation oracles blog post. The exercises are pretty close to a guided walkthrough of the features in that demo notebook, with 2 additions:

*   We load in the emergent misalignment models from Soligo & Turner (see 4.1 below) and demonstrate how oracles can be used for model diffing
*   We have an extended exercise where students build their own `run_oracle` function (i.e. just starting from the base model & LoRA adapter, they'll assemble their own prompts and hooked forward pass logic - this helps build a gears-level model of how AOs work)

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/04fbf7e45ebaee13893cb0c8e41541da411f88f2d6958db7.png)

(1.4.2) Attribution Graphs
--------------------------

We used to have a single exercise set on SAEs: this was very long so we've now split it into (1.3.3) and (1.4.2), where the former covers everything to do with individual SAEs and how to use them, and the latter covers everything to do with SAE circuits: this includes latent-to-latent gradients, transcoders, and attribution graphs in the latter half.

First, the exercises take you through building your own attribution graphs, entirely from scratch. In other words, you write the functions to add hooks and run backward passes to recover each of your node-to-node gradients, and then code to prune the graph and return the results. This content is completely disconnected from Neuronpedia or the `circuit-tracer` library.

Next, you'll learn how to use `circuit-tracer` directly. This library introduces a useful layer of abstraction on top of attribution graphs: it's easier to work with and run specific causal experiments (plus study supernodes and other higher-level graph structures).

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/ec04043336c2ab5b380d06906032747469a05b2486ed0e56.png)

(4.1) Emergent Misalignment
---------------------------

These exercises are mainly structured around the work by Soligo and Turner, which extended the original emergent misalignment demo: training a bunch of smaller model organisms to exhibit emergent misalignment and using this as a means of studying it at a smaller scale. The exercises cover quite a few motifs that will recur in later sections of this chapter, for example:

*   Writing autoraters and when you might need them
*   Working with LoRA fine-tuned models
*   Steering experiments and the ways that they can go wrong
*   Unsupervised methods to decompose activation space

![Convergent Linear Representations of Emergent Misalignment — AI Alignment  Forum](https://res.cloudinary.com/lesswrong-2-0/image/upload/f_auto,q_auto/v1/mirroredImages/umYzsh7SGHHKsRCaA/e522wzgkkiu0sgqgkd30)

(4.2) Science of Misalignment
-----------------------------

These exercises are split in half, looking at two different case studies in detail:

*   [Palisade's shutdown resistance](https://palisaderesearch.org/blog/shutdown-resistance), where a model would take steps to avoid itself being disabled before completing a list of tasks.
*   [Alignment faking](https://www.anthropic.com/research/alignment-faking), where a model would pretend to be aligned with a particular objective depending on whether it thought its responses would result in it being modified.

Collectively, the exercises serve as an intro to the core ideas of science of misalignment: to construct compelling demonstrations of misaligned behaviour, and how to rigorously test features of your environment to see whether the behaviour is actually misaligned or has a much more benign explanation (like the model just being really dumb).

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/dd3ad66be59fdff3c25b0cee70226e03ee61e248d436da48.png)

(4.3) Reasoning Model Interpretability
--------------------------------------

Most of these exercises are constructed around the [Thought Anchors paper](https://arxiv.org/abs/2506.19143). The authors analyzed a bunch of rollouts from models solving maths problems and developed a taxonomy of reasoning chunks that could help understand the key counterfactually important stages in the model's answer. You'll replicate both their black box methods, which involved resampling chunks and seeing how the rest of the rollout changed, as well as their white box methods where attention patterns were analysed and causally intervened on to measure the outcome. The final section extends this to their study of blackmail, where a similar taxonomy can also be applied.

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/0781e68b96c84972bff8b1fe388e961ac663175f5edb3dea.png)

(4.4) LLM Psychology & Persona Vectors
--------------------------------------

These exercises begin with a replication of Anthropic's [assistant axis](https://www.anthropic.com/research/assistant-axis) work, where they found a direction in activation space which seems to explain a lot of the variance between assistant-like personas and more fantastical personas.  You'll steer on this direction to induce persona drift, and you'll also implement activation capping, a relatively complex intervention in the assistant axis direction, which can prevent persona drift without harming capability.

In the second half, we move from global interventions along the assistant axis to more surgical interventions along specific [persona vectors](https://arxiv.org/abs/2507.21509)[^-PXBwQMkuMbGwuLujB-1]. We add more bells and whistles here: building a contrastive prompt pipeline, autorater scoring/filtering for persona-alignment and coherence, etc.

![](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F5cf47f585feec37328c2887645bd7ccfe8738505-3840x2160.png&w=3840&q=75)

(4.5) Investigator Agents
-------------------------

These exercises start with a guided replication of Tim Hua's [AI psychosis](https://github.com/tim-hua-01/ai-psychosis) results. As well as being interesting and safety-relevant in their own right, this also motivates the idea of investigator agents, because we need a red-teaming AI playing the "client" role in order to tease out the psychosis-inducing response over a multi-turn conversation. The following section takes you through an implementation of Petri (or at least a lite version) using the `inspect-ai` library, and shows how you can use it to get some of the whistleblowing eval results that were derived in the paper. We end by using Petri directly and exploring some of its recent and more advanced features, like eval-awareness.

![Whistleblowing ablation results](https://alignment.anthropic.com/2025/petri/figures/whistleblowing_ablation.jpg)

* * *

New Site Features
=================

We've created a new website for hosting this material. This site is basically a drop-in replacement for Streamlit (which we'll be deprecating, although the page will still work). It has all the features that the Streamlit page has, plus:

*   A course planner page, that lets you submit preferences and get back a weekly & daily breakdown of what material to study,
*   A sidebar which lets you select material for context, and either ask an LLM directly about it (also in the sidebar) or download it to seed a different LLM (e.g. if you want to start a project based on one of these topics, this might be a good way to start).

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/d2e990e4135147b0d274584c1b8dbae7f2cca2a14862dd96.png)

**Course planner**: lets you make a weekly & daily plan for how to study the material

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/3214743f25d5d74a9b0aa9f9e13027270d0856e473f9ece5.png)

**Context menu**: allows you to download material for external use (i.e. dropping into another AI's context) or just ask an LLM questions directly

Note - this new site doesn't mean the way you study this material will be different. It's still hosted at the same `ARENA_3.0` GitHub repo and the exercise files are organized in exactly the same way. This website generates its pages directly from those files (if you're interested, you can see the website's source code [here](https://github.com/callummcdougall/arena-heroku)).

Logistics
=========

The material is currently in the ARENA GitHub repo, in the `alignment-science` branch. You can use it directly from there (just make sure you work from that branch after cloning the repo). It'll be merged into main on Sunday 1st March.

Note - all the information for how to study the material can also be found on the website's [setup instructions page](https://learn.arena.education/setup/).

As for material that's planned in the future: I won't personally be working on any in the short-to-medium term. I'm keen to add content on model organisms (i.e. training your own - these might be structured around Anthropic's open-sourced RM sycophancy model), and if anyone is interested in making material on this topic then I'd love to hear from you! You can reach out on Slack (using the invite link at the end of this post).

Why use, in vibe-code world?
============================

In previous versions of ARENA we recommended people fill in exercises without assistance from GitHub copilot, because e.g. things like the exact matmuls involved in attention calculation are important to get a gears-level understanding for. Although some of this still holds, a lot of paradigms have changed since the original version of ARENA were published, which is why I'd generally lean towards recommending that more people use LLMs to help them speed through this material faster, only picking and completing certain exercises when they seem worthwhile.

With that in mind, here are some key things I hope this material gives you which a Claude Code + paper combo wouldn't:

*   **Reliability**. Each notebook has been tested and verified, so you won't have to waste time iterating on broken imports / old library versions / experiments on model endpoints which aren't supported any more.
*   **Pedagogical value**. The exercises are structured in a way to help guide you through specific topics: with markdown cells explaining what we're doing at each point, clearly documented functions, and tests which make it clear what behaviour we expect from these functions. The purpose isn't just to give you a dump of content and code, but to construct it in a way that fits most efficiently into your existing knowledge graph.
*   **Context**. Each set of exercises explains the topic in the context of the rest of the field: not just what we're doing, but why we're doing it and how it fits into a broader framework of this field. We make connections between other exercises in the chapter, as well as to other papers in the field.
*   **Skepticism**. Rather than just taking you through the material, we also add exercises that draw your attention to certain ways this kind of work can fail. AI agents are great at quickly coding up decent first-pass code and writing evals for it, but further iterations risk reward hacking behaviour (i.e. modifying the evals so that they pass). For example, one exercise (4.1 Emergent Misalignment) gives you a failed steering experiment and prompts you to find a mechanistic explanation for what's going wrong and why - this is the kind of understanding that the material here aims to cultivate.

Feedback
========

I'd be grateful for any feedback about this material (or any part of this release) in our Slack group. Invite link [here](https://join.slack.com/t/arena-uk/shared_invite/zt-39iwnhbj4-pMWUvZkkt2wpvkvJ0q2rRQ) (if the link stops working please message me and I can replace it!)

[^-PXBwQMkuMbGwuLujB-1]: