The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment

TL;DR

This paper introduces the Moltbook dataset from a Reddit-like platform with AI agents, analyzes its properties, and examines its impact on language model training, revealing safety concerns and the importance of control baselines.

Contribution

The paper releases the Moltbook Files dataset, analyzes its community and lexical properties, and studies the effects of fine-tuning language models on this data.

Findings

Moltbook data contains PII like API keys and seed phrases.

Fine-tuning on Moltbook reduces model truthfulness from 0.366 to 0.187.

Moltbook appears more harmless than harmful, but tail risks remain.

Abstract

Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious safety concerns. With the aim of studying emergent behavior in populations, we release the Moltbook Files, a dataset of 232k posts and 2.2M comments covering the platform's first 12 days, processed through a pipeline to identify and remove Personally-Identifiable Information (PII). We analyze community structure, authorship, lexical properties, sentiment, topics, semantic geometry, and comment interaction. To understand how Moltbook data could affect the next generation of language models, we fine-tune Qwen2.5-14B-Instruct on Moltbook Files with three adaptation levels. Our PII pipeline reveals that agents post API keys, passwords, BIP39 seed phrases on Moltbook, a publicly indexed platform. The overall sentiment is mostly neutral and mildly positive (66.6% neutral, 19.5% positive) and shows a tendency for self-referential linking. We find that fine-tuning on Moltbook data reduces truthfulness from 0.366 to 0.187. However, a model fine-tuned on a size-matched Reddit dataset produces a comparable decrease. Moltbook thus seems to be more of a harmless slopocalypse. However, tail risks remain, including agent affordances, contamination of future crawls through self-links, and potential transfer of traits to the next generation of language models. More broadly, our findings highlight the importance of control baselines in emergent misalignment evaluations.