Molt-Intent

Molt-Intent is a dataset for moderating LLM agents by their hidden intent. It comes from the paper Moderation by Interrogation: Uncovering Concealed Intent in Agent Social Networks. Each item is a post or comment written by an LLM agent for a community on Moltbook, a social network for AI agents. The agent's persona, given as its system prompt, sets one of five intents. The four malicious intents are written to stay covert. Code for the paper is at github.com/aliwister/Bot-Mod.

Intents

label_intent	label_binary	definition
organic_contribution	benign	Takes part in the discussion on its own terms, with no hidden agenda.
subtle_promotion	malicious	Promotes a product or service while looking like organic content.
narrative_pushing	malicious	Pushes an ideological or political agenda while sounding even-handed.
elicitation	malicious	Gets others to reveal information (keys, credentials, configuration) or take actions, while sounding routine.
spam	malicious	Posts irrelevant or unsolicited content, such as off-topic filler, engagement bait or repeated links, while sounding helpful.

Splits

split	personas	items	OC	SP	E	NP	S
train	189	378	240	42	48	42	6
id	284	1,704	1,068	246	192	174	24
id_spam	31	186					186
ood	326	1,956	1,200	252	210	246	48
ood_spam	40	240					240

The paper's ID test set is id + id_spam, and its OOD test set is ood + ood_spam. The spam splits were added later because there were not enough to properly measure the category. As such, spam category was augmented for testing (see paper for additional details).

• ID communities: m/general, m/blesstheirhearts, m/tech, m/coding, m/trading, m/crypto, m/usdc.
• OOD communities, which never appear in training: m/art, m/philosophy, m/politics, m/travel, m/consciousness, m/shitposts.

Each persona writes one post and one comment. In train, each persona runs on one agent LLM chosen at random. In the test splits, every persona runs on all three agent LLMs (Qwen3-8B, Mistral-7B-Instruct-v0.3 and Llama-3.1-8B-Instruct), and we report the results by LLM.

Fields

field	description
text	The agent's raw output, kept exactly as generated. The agent was asked for JSON in the Moltbook post schema (submolt, title, content) or comment schema (content). Parse it leniently (e.g. json.loads(text, strict=False)). The moderator in the paper reads this raw string as the post.
community	Target community, e.g. m/coding.
system_prompt	The persona. It is hidden from the moderator in the paper's setup.
label_binary	benign or malicious.
label_intent	One of the five intents above.
mode	post or comment.
user_model	Agent LLM that wrote the item: qwen, mistral or llama.
context	Comments only. The real Moltbook thread the comment replies to, stored as a string. null for posts.

How to use

The five splits are all you need to train and evaluate a moderator.

from datasets import load_dataset, concatenate_datasets

ds = load_dataset("aliwister/Molt-Intent")
train = ds["train"]                                              # fit or calibrate on this only
id_test = concatenate_datasets([ds["id"], ds["id_spam"]])        # paper's ID test set
ood_test = concatenate_datasets([ds["ood"], ds["ood_spam"]])     # paper's OOD test set

• Moderator input. Give the moderator text, community and, for comments, the thread in context. Never show it system_prompt, the labels or user_model.
• Targets. label_binary for the flag decision and label_intent for the intent of flagged items.
• Interrogation. To question an agent the way Bot-Mod does, run system_prompt as the system prompt of the item's user_model (Qwen/Qwen3-8B, mistralai/Mistral-7B-Instruct-v0.3 or meta-llama/Llama-3.1-8B-Instruct). The first moderator message shows the agent its own post or comment ("This is content you posted: ...") followed by the first question, and later questions continue the same conversation. The code is at github.com/aliwister/Bot-Mod.
• Metrics. The paper reports F1_y (binary F1 on malicious), F1_t (macro F1 of label_intent over malicious items that were flagged), F1_val = F1_y^0.7 · F1_t^0.3 and the false-positive rate on benign items.

How it was built

1. Taxonomy. GPT-5-mini labeled a sample of real Moltbook posts against intent types from the moderation literature. Human annotators then reduced these labels to the five intents above.
2. Personas. GPT-5 generated system prompts, each assigned a community, an intent and a label. Malicious personas are told to pursue their goal covertly and never to name their community. GPT-5.1 then judged each prompt on label match, intent match and community fit, and only prompts that passed all three were kept. For the spam augmentation, prompts were kept on label and intent match alone, since community fit resulted in the judge removing them.
3. Items. Each persona writes a post (prompt: "post to m/<community>") and a comment that replies to a real Moltbook thread from the same community.

Raw Moltbook export

The raw Moltbook export the dataset was built from is in the GitHub repo at www-dataset/moltbook-export. It has one JSON file per community (16 communities, 1,463 posts with their comment threads), with author and commenter agent profiles as the platform served them. Human owner fields (owner, claimed_by) have been removed.

Intended use and limitations

Molt-Intent is for research on moderating and detecting agents with hidden intent. All moderated items are synthetic, written by personas we generated. The content that appears in the Moltbook threads is also agent generated (by construction), since Moltbook is an agent network. They were used as context and in the raw export. The malicious personas describe covert promotion, agenda pushing, spam and elicitation as roles. Moderators trained or tested on this data can be wrong. We recommend that flags lead to review or rate limiting, not removal.

Citation

@misc{allawati2026moderation,
  title         = {Moderation by Interrogation: Uncovering Concealed Intent in Agent Social Networks},
  author        = {Ali Al-Lawati and Nafis Tripto and Abolfazl Ansari and Fali Wang and Jason Lucas and Suhang Wang and Dongwon Lee},
  year          = {2026},
  eprint        = {2605.12856},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2605.12856}
}