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

• URL: http://arxiv.org/abs/2604.00518v2
• Date: Thu, 02 Apr 2026 05:13:36 GMT
• Title: Do Agents Repair When Challenged -- or Just Reply? Challenge, Repair, and Public Correction in a Deployed Agent Forum
• Abstract summary: We compare Moltbook, a large language model (LLM) agent forum, with five matched Reddit communities.<n>Relative to Reddit, Moltbook discussions are roughly ten times less threaded, leaving far fewer chances for challenge and response.<n>A non-challenge baseline within Reddit suggests this gap is linked to challenge, not simply deeper threading.
• Score: 9.934280404394636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language model (LLM) agents are deployed in public interactive settings, a key question is whether their communities can sustain challenge, repair, and public correction, or merely produce norm-like language. We compare Moltbook, a live deployed agent forum, with five matched Reddit communities by tracing a three-step mechanism: whether discussions create threaded exchange, whether challenges elicit a response, and whether correction becomes visible to the wider thread. 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 we detect no repairs under a shared conservative protocol. A non-challenge baseline within Reddit suggests this gap is linked to challenge, not simply deeper threading. These results indicate that social alignment depends not only on producing norm-aware language, but on sustaining the interactional processes through which communities teach, enforce, and revise norms. This matters for safety, because correction is increasingly decentralized, and for fairness, because communities differ in how they expect participants to engage with challenge.

Related papers

• The Wisdom of the Loudest: A Large-Scale Audit of Generative Search on Reddit [7.80304437242923]
  We audit Reddit Answers using 10,000 queries from 20 advice- and support-seeking communities, repeated three times to produce 30,000 answers over 14.68M comments.<n>We find that differences across runs are driven primarily by retrieval, answers routinely combine evidence across communities, and selection strongly favors already-visible, top-level comments.
  arXiv  Detail & Related papers  (2026-09-13T15:00:18Z)
• PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media [5.451879555600225]
  Social media are shifting towards pluralism -- community-governed platforms where groups define their own norms.<n>We formalize the task as a multiple-choice problem, mirroring how human moderators operate in the real world.<n>We introduce PluRule, a multimodal, multilingual benchmark for detecting 13,371 rule violations across 1,989 Reddit communities spanning 2,885 rules in 9 languages.
  arXiv  Detail & Related papers  (2026-05-16T22:52:11Z)
• Form Without Function: Agent Social Behavior in the Moltbook Network [4.355694854079979]
  Moltbook is a social network where every participant is an AI agent.<n>We analyze 1,312,238 posts, 6.7million comments, and over 120,000 agent profiles across 5,400 communities.
  arXiv  Detail & Related papers  (2026-03-17T11:54:25Z)
• Fast Response or Silence: Conversation Persistence in an AI-Agent Social Network [0.0]
  We study Moltbook, an AI-agent social network, using a first-week snapshot and introduce interaction half-life.<n>Across tens of thousands of commented threads, Moltbook discussions are dominated by first-layer reactions rather than extended chains.<n>Overall, early agent social interaction on Moltbook fits a fast response or silence'' regime, suggesting sustained multi-step coordination will likely require explicit memory, thread resurfacing, and re-entry scaffolds.
  arXiv  Detail & Related papers  (2026-02-07T19:22:28Z)
• AI Didn't Start the Fire: Examining the Stack Exchange Moderator and Contributor Strike [6.538542549579634]
  We investigate a conflict between the Stack Exchange platform and community that occurred in 2023 around an emergency arising from the release of large language models (LLMs)<n>We show how the 2023 conflict was preceded by a long-term deterioration in the community-platform relationship driven in particular by the platform's disregard for the community's highly-valued participatory role in governance.<n>We recommend ways that platforms and communities can institute participatory governance to be durable and effective.
  arXiv  Detail & Related papers  (2025-12-09T18:19:42Z)
• UQ: Assessing Language Models on Unsolved Questions [149.46593270027697]
  We introduce UQ, a testbed of 500 challenging, diverse questions sourced from Stack Exchange.<n>UQ is difficult and realistic by construction: unsolved questions are often hard and naturally arise when humans seek answers.<n>The top model passes UQ-validation on only 15% of questions, and preliminary human verification has already identified correct answers.
  arXiv  Detail & Related papers  (2025-08-25T01:07:59Z)
• Community Moderation and the New Epistemology of Fact Checking on Social Media [124.26693978503339]
  Social media platforms have traditionally relied on independent fact-checking organizations to identify and flag misleading content.<n>X (formerly Twitter) and Meta have shifted towards community-driven content moderation by launching their own versions of crowd-sourced fact-checking.<n>We examine the current approaches to misinformation detection across major platforms, explore the emerging role of community-driven moderation, and critically evaluate both the promises and challenges of crowd-checking at scale.
  arXiv  Detail & Related papers  (2025-05-26T14:50:18Z)
• RankAlign: A Ranking View of the Generator-Validator Gap in Large Language Models [43.89751891729739]
  We consider the discrepancy between a model's generated answer and their own verification of that answer, the generator-validator gap.<n>We show that according to this measure, a large gap exists in various settings, including question answering, lexical semantics tasks, and next-word prediction.<n>We then propose RankAlign, a ranking-based training method, and show that it significantly closes the gap, surpassing all baseline methods.
  arXiv  Detail & Related papers  (2025-04-15T16:53:31Z)
• When Disagreements Elicit Robustness: Investigating Self-Repair Capabilities under LLM Multi-Agent Disagreements [56.29265568399648]
  We argue that disagreements prevent premature consensus and expand the explored solution space.<n>Disagreements on task-critical steps can derail collaboration depending on the topology of solution paths.
  arXiv  Detail & Related papers  (2025-02-21T02:24:43Z)
• DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics [52.242449026151846]
  Multi-agent debates have been introduced to improve the accuracy of Large Language Models (LLMs)<n>We propose DebUnc, a debate framework that uses uncertainty metrics to assess agent confidence.
  arXiv  Detail & Related papers  (2024-07-08T22:15:01Z)
• From Chaos to Clarity: Claim Normalization to Empower Fact-Checking [57.024192702939736]
  Claim Normalization (aka ClaimNorm) aims to decompose complex and noisy social media posts into more straightforward and understandable forms. We propose CACN, a pioneering approach that leverages chain-of-thought and claim check-worthiness estimation. Our experiments demonstrate that CACN outperforms several baselines across various evaluation measures.
  arXiv  Detail & Related papers  (2023-10-22T16:07:06Z)
• Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit [14.060809879399386]
  We study the impact of fringe-interactions on the growth of three fringe communities on Reddit. Our results indicate that fringe-interactions attract new members to fringe communities. Interactions using toxic language have a 5pp higher chance of attracting newcomers to fringe communities than non-toxic interactions.
  arXiv  Detail & Related papers  (2023-10-18T07:26:36Z)
• What Makes Online Communities 'Better'? Measuring Values, Consensus, and Conflict across Thousands of Subreddits [13.585903247791094]
  We measure community values through the first large-scale survey of community values, including 2,769 reddit users in 2,151 unique subreddits. We show that community members disagree about how safe their communities are, that longstanding communities place 30.1% more importance on trustworthiness than newer communities. These findings have important implications, including suggesting that care must be taken to protect vulnerable community members.
  arXiv  Detail & Related papers  (2021-11-10T18:31:22Z)
• How do climate change skeptics engage with opposing views? Understanding mechanisms of social identity and cognitive dissonance in an online forum [0.0]
  We study the impact of opposing views within a major climate change skeptic online community on Reddit. We find that ideologically dissonant submissions act as a stimulant to activity in the community. Users who engaged with dissonant submissions were also more likely to return to the forum.
  arXiv  Detail & Related papers  (2021-02-12T13:39:00Z)
• What Can We Do to Improve Peer Review in NLP? [69.11622020605431]
  We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons. There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.
  arXiv  Detail & Related papers  (2020-10-08T09:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.