✨When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation
📝 Summary:
Reasoning-enhanced LLMs can over-optimize, making them better problem solvers but poor simulators of diverse, boundedly rational behavior. This solver-sampler mismatch means high model capability hurts simulation fidelity. Bounded reflection improves realism.
🔹 Publication Date: Published on Apr 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11840
• PDF: https://arxiv.org/pdf/2604.11840
• Project Page: https://www.sandric.co
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For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #MultiAgentSystems #BehavioralSimulation #AI #AgentBasedModeling
📝 Summary:
Reasoning-enhanced LLMs can over-optimize, making them better problem solvers but poor simulators of diverse, boundedly rational behavior. This solver-sampler mismatch means high model capability hurts simulation fidelity. Bounded reflection improves realism.
🔹 Publication Date: Published on Apr 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11840
• PDF: https://arxiv.org/pdf/2604.11840
• Project Page: https://www.sandric.co
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#LLM #MultiAgentSystems #BehavioralSimulation #AI #AgentBasedModeling