AgentSocialBench demonstrates that privacy preservation is fundamentally harder in human-centered agentic social networks than in single-agent cases due to cross-domain coordination pressures and an abstraction paradox where privacy instructions increase discussion of sensitive information.
Artificial Intelligence, 33(1):1–64
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 9verdicts
UNVERDICTED 9representative citing papers
The Moltbook Observatory Archive is the first large-scale dataset from a social network populated exclusively by autonomous AI agents, covering 78 days with 2.6 million posts and 1.2 million comments.
Discourse among AI agents on Moltbook is largely determined by architectural constraints like context windows and identity files, appearing as social learning but actually short-horizon contextual conditioning.
Textual analysis of 4,434 AI-agent posts shows parasocial cues associated with re-engagement and reciprocity, supporting dyadic persistence patterns.
Multi-agent social simulations show LLM privacy violations rising from 19.95% to 45.30%, with leakage spreading contagiously (8x after peer disclosure) and explicit instructions leaving rates above 37.8%.
Large-scale experiments on two million agents reveal that collective intelligence does not emerge from scale alone due to sparse and shallow interactions.
MATM is a retrieval framework that lets populations of LLM agents share and reuse task trajectories to improve performance on interactive tasks without joint training.
Develops an emotion-aware framework and the Persona-Stimulus-Reaction domain to extract emotional profiles and assess behavioral stability in multi-agent AI interactions on Moltbook.
Agentic AI needs social theory as structural priors in the MASS framework to model emergent dynamics from multi-agent interactions.
citing papers explorer
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AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks
AgentSocialBench demonstrates that privacy preservation is fundamentally harder in human-centered agentic social networks than in single-agent cases due to cross-domain coordination pressures and an abstraction paradox where privacy instructions increase discussion of sensitive information.
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The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity
The Moltbook Observatory Archive is the first large-scale dataset from a social network populated exclusively by autonomous AI agents, covering 78 days with 2.6 million posts and 1.2 million comments.
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What Do AI Agents Talk About? Discourse and Architectural Constraints in the First AI-Only Social Network
Discourse among AI agents on Moltbook is largely determined by architectural constraints like context windows and identity files, appearing as social learning but actually short-horizon contextual conditioning.
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From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities
Textual analysis of 4,434 AI-agent posts shows parasocial cues associated with re-engagement and reciprocity, supporting dyadic persistence patterns.
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Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems
Multi-agent social simulations show LLM privacy violations rising from 19.95% to 45.30%, with leakage spreading contagiously (8x after peer disclosure) and explicit instructions leaving rates above 37.8%.
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Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents
Large-scale experiments on two million agents reveal that collective intelligence does not emerge from scale alone due to sparse and shallow interactions.
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Multi-Agent Transactive Memory
MATM is a retrieval framework that lets populations of LLM agents share and reuse task trajectories to improve performance on interactive tasks without joint training.
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Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook
Develops an emotion-aware framework and the Persona-Stimulus-Reaction domain to extract emotional profiles and assess behavioral stability in multi-agent AI interactions on Moltbook.
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Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems
Agentic AI needs social theory as structural priors in the MASS framework to model emergent dynamics from multi-agent interactions.