When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation

arXiv:2604.11840v2 Announce Type: replace Abstract: Behavioral simulation and strategic problem solving are different tasks. Large language models are increasingly explored as agents in policy-facing institutional simulations, but stronger reasoning need not improve behavioral sampling. We study this solver-sampler mismatch in three multi-agent negotiation environments: two trading-limits scenarios with different authority structures and a grid-curtailment case in emergency electricity management. Across two primary model families, native reasoning and often no reflection collapse toward authority-heavy outcomes. The sharpest case is DeepSeek native reasoning in the grid-curtailment transfer: it reaches action entropy 1.256 and a concession-arc rate of 0.933, yet still ends in authority decision in 15 of 15 runs. A direct OpenAI extension shows the same pressure at provider breadth: GPT-5.2 native reasoning ends in authority decisions in 45 of 45 runs across the three environments. Budget-matched no-reflection controls and orthogonal private-state controls remain rigid, while the negotiation-structured scaffold condition is the only condition that consistently opens negotiated outcomes. These diagnostics are failure screens within a fixed negotiation grammar, not evidence of external behavioral realism or policy-forecasting validity. The results show that neither more output space nor generic extra private state rescues solver-like sampler failure. For institutional simulation, solver strength and sampler qualification are different objectives: models should be evaluated for the behavioral role they are meant to play, not only for strategic capability.