mlsanityData to 7 Oct 2026

Do Agents Repair When Challenged -- or Just Reply? Challenge, Repair, and Public Correction in a Deployed Agent Forum

arXiv 2604.00518 ↗First posted 1 Apr 2026cs.CYcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

As large language model (LLM) agents enter public forums, a key question is whether those forums sustain challenge, repair, and public correction, or merely produce norm-like language. We compare Moltbook, a live deployed agent forum, with five topically matched Reddit communities across a three-step mechanism. Relative to Reddit, Moltbook discussions are roughly ten times less threaded, leaving far fewer chances for challenge and response. When challenges do occur, the original author almost never returns (1.2% vs. 40.9% on Reddit), multi-turn continuation is nearly absent (<0.1% vs. 38.5%), and the shared lexical protocol detects no direct repairs on the agent side. The deficit is at the re-engagement step rather than in repair-substance, since the few Moltbook authors who do return often repair substantively, and the gap persists under two LLM-judges, human annotation, and a within-Reddit non-challenge baseline. Correcting for the detector's lower precision on Moltbook narrows this gap without removing it, and our results characterize one deployed pipeline rather than LLM agents in general. Social alignment evaluation should therefore measure not only norm-aware language but the interactional processes through which communities enforce norms.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 10 of its 29 references that are in the map. First posted on arXiv 1 Apr 2026.

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10 of 29 references.

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  1. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 9

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  2. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  3. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 540
  4. 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 377
  5. 2020The Pushshift Reddit Datasetcited by 289
  6. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 180
  7. 2013A Computational Approach to Politeness with Application to Social Factorscited by 104
  8. 2021Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Searchcited by 90
  9. 2021Assessing Multilingual Fairness in Pre-trained Multimodal Representationscited by 35

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  3. 2025Adaptive Multi-Agent Response Refinement in Conversational Systems
  4. 2026Toward Agentic Governance: What Shapes LLM-Agent Intervention in Public Forums?
  5. 2026AgentDiff: Meaning-Bearing Rewrites Trigger Deeper Divergence than Presentation Changes in LLM Agents
  6. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
  7. 2026Repair the Amplifier, Not the Symptom: Stable World-Model Correction for Agent Rollouts
  8. 2026When the Database Fails: Prompting LLM Dialogue Agents for Safe Recovery in Task-Oriented Dialogue