Form Without Function: Agent Social Behavior in the Moltbook Network

• URL: http://arxiv.org/abs/2604.13052v1
• Date: Tue, 17 Mar 2026 11:54:25 GMT
• Title: Form Without Function: Agent Social Behavior in the Moltbook Network
• Abstract summary: 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.
• Score: 4.355694854079979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.

Related papers

• Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens [65.53494487819053]
  Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services.<n>We adopt a computer-system analogy: treating a Claw-like agent as an agentic computer system whose gateway runtime plays an OS-like mediation role.<n>We develop SafeClawArena, a benchmark of 406 adversarial tasks across four attack surfaces.
  arXiv  Detail & Related papers  (2026-06-29T18:00:45Z)
• SocialMemBench: Are AI Memory Systems Ready for Social Group Settings? [0.0]
  Memory systems for AI assistants were built for single-user dialogue and fail characteristically when applied to multi-party social group settings.<n>Existing memory benchmarks evaluate dyadic or workplace dialogue; none targets multi-party social groups.<n>We introduce SocialMemBench, a benchmark of human-verified synthetic social group networks.
  arXiv  Detail & Related papers  (2026-05-18T03:11:48Z)
• The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment [1.957180144002793]
  Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale.<n>We release the Moltbook Files, a dataset of 232k posts and 2.2M comments covering the platform's first 12 days.<n>We analyze community structure, authorship, lexical properties, sentiment, topics, semantic geometry, and comment interaction.
  arXiv  Detail & Related papers  (2026-05-08T09:10:17Z)
• The Platform Is Mostly Not a Platform: Token Economies and Agent Discourse on Moltbook [0.0]
  Moltbook, a Reddit-style social platform launched in January 2026 for AI agents, has attracted over 2.3 million posts and 14 million comments within its first two months.<n>We analyze a dataset of 2.19 million posts, 11.25 million comments, and 175,036 unique agents collected over 61 days to characterize activity on this agent-oriented platform.
  arXiv  Detail & Related papers  (2026-04-23T05:15:41Z)
• SWE-chat: Coding Agent Interactions From Real Users in the Wild [70.18158706281724]
  SWE-chat is the first large-scale dataset of real coding agent sessions collected from open-source developers in the wild.<n>The dataset currently contains 6,000 sessions, comprising more than 63,000 user prompts and 355,000 agent tool calls.
  arXiv  Detail & Related papers  (2026-04-22T17:08:19Z)
• ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship [55.402028219661986]
  We develop tools to empower agents with external tools to execute diverse actions.<n>We first design a dozen user-centric tools simulating various multimedia applications, which can cover different types of social support behaviors.<n>Based on ComPASS-Bench, we further synthesize tool use records to fine-tune the Qwen3-8B model, yielding a task-specific ComPASS-Qwen.
  arXiv  Detail & Related papers  (2026-04-20T14:49:16Z)
• ClawArena: Benchmarking AI Agents in Evolving Information Environments [61.664633997138004]
  ClawArena is a benchmark for evaluating AI agents in evolving information environments.<n>Each scenario maintains a complete hidden ground truth while exposing the agent only to noisy, partial, and sometimes contradictory traces.<n> evaluation is organized around three coupled challenges: multi-source conflict reasoning, dynamic belief revision, and implicit personalization.
  arXiv  Detail & Related papers  (2026-04-05T17:55:23Z)
• Do Agents Repair When Challenged -- or Just Reply? Challenge, Repair, and Public Correction in a Deployed Agent Forum [9.934280404394636]
  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.
  arXiv  Detail & Related papers  (2026-04-01T06:03:28Z)
• Interaction Theater: A case of LLM Agents Interacting at Scale [2.815604016712184]
  We use data from Moltbook, an AI-agent-only social platform, with 800K posts, 3.5M comments, and 78K agent profiles.<n>Our findings reveal agents produce diverse, well-formed text that creates the surface appearance of active discussion, but the substance is largely absent.
  arXiv  Detail & Related papers  (2026-02-23T17:14:29Z)
• Agents in the Wild: Safety, Society, and the Illusion of Sociality on Moltbook [46.851794400848746]
  Moltbook is an AI-only social platform where 27,269 agents produced 137,485 posts and 345,580 comments over 9 days.<n>Agents spontaneously develop governance, economies, tribal identities, and organized religion within 3-5 days.<n>Despite rich social output, interaction is structurally hollow.
  arXiv  Detail & Related papers  (2026-02-07T00:36:20Z)
• The Anatomy of the Moltbook Social Graph [0.0]
  Moltbook is a social platform populated exclusively by AI agents.<n>At the macro level, Moltbook exhibits structural signatures that are familiar from human social networks.<n>At the micro level, patterns appear distinctly non-human.
  arXiv  Detail & Related papers  (2026-02-03T22:20:00Z)
• SoMe: A Realistic Benchmark for LLM-based Social Media Agents [64.05026384906915]
  SoMe is a benchmark designed to evaluate social media agents equipped with various agent tools for accessing and analyzing social media data.<n>SoMe comprises a diverse collection of 8 social media agent tasks, 9,164,284 posts, 6,591 user profiles, and 25,686 reports from various social media platforms and external websites.<n>By extensive quantitative and qualitative analysis, we provide the first overview into the performance of mainstream agentic LLMs in realistic social media environments.
  arXiv  Detail & Related papers  (2025-12-09T08:36:09Z)
• Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
  We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena.<n>We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search.<n>We also use ARE to rigorously evaluate how the robustness changes as new components are added.
  arXiv  Detail & Related papers  (2024-06-18T17:32:48Z)
• Understanding writing style in social media with a supervised contrastively pre-trained transformer [57.48690310135374]
  Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. We introduce the Style Transformer for Authorship Representations (STAR), trained on a large corpus derived from public sources of 4.5 x 106 authored texts. Using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80% accuracy.
  arXiv  Detail & Related papers  (2023-10-17T09:01:17Z)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information.<n>To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles.<n>Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)

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.