arXiv AI
Partner-Specific Affective Precision in Social Active Inference
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Partner-Specific Affective Precision in Social Active Inference
In multi-agent social settings, model reliability varies across relationships. Beyond inferring what others will do, an agent must calibrate how confidently those inferences should guide policy select...
arXiv AI
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Evaluation of Multi-Turn Consistency in LLM Agents: Survival Analysis and Failure-Rationale Taxonomy
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arXiv AI
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Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating
The study examines how users of a major dating platform respond to autonomous LLM agents that converse on their behalf. Using two large surveys, researchers built a latent-variable model showing that willingness to send and receive agent-mediated messages are highly correlated yet distinct. The findings reveal a delegation asymmetry: users are more willing to deploy their own agent than to engage with others’ agents, leading to low overall reciprocity and gender‑directional imbalances in agent interactions.
arXiv AI
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Trust Between AI Agents: Measuring Formation, Breakage, and Recovery, with Implications for Governing Multi-Agent Systems
arXiv:2606. 14923v1 Announce Type: new Abstract: As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates.
arXiv AI
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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning
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arXiv AI
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Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models
The study investigates how reputation, strategy, and emotional signals influence cooperation in generative AI models using the iterated prisoner's dilemma. Non‑reasoning models (Claude 3.5, Gemini 2.0 Flash, GPT‑4o) showed cooperation shaped by all three factors, while reasoning models (Claude 4.6, Gemini 3, GPT‑5.2) relied more on strategy and reputation, displayed reduced emotional influence, and exhibited varied end‑game behaviors. These results highlight the growing sophistication and heterogeneity of AI social behavior, suggesting the need for standardized cooperation benchmarks.