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.
Does socialization emerge in AI agent society? A case study of Moltbook
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
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2026 6verdicts
UNVERDICTED 6representative 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.
Large-scale experiments on two million agents reveal that collective intelligence does not emerge from scale alone due to sparse and shallow interactions.
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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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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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.