APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents

• URL: http://arxiv.org/abs/2605.27419v1
• Date: Tue, 19 May 2026 08:45:41 GMT
• Title: APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents
• Abstract summary: We propose a framework that reframes scalable LLM-based simulation as a recurrent oracle-allocation problem.<n> Adaptive Prototype Simulation (APS) queries adaptive core prototypes, selected singleton-tail agents, and shadow-audit agents.<n>APS achieves a 381.1-fold reduction over full simulation, with reference-aligned final-round JSD of 0.094 against the corresponding full-LLM reference.
• Score: 23.922499684047263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-agent simulation offers a flexible computational tool for studying population response trajectories that depend on scenario events, memory, demographics, and evolving social context. However, full multi-round simulation scales linearly with both population size and horizon, requiring every agent to query the LLM at every round. We propose Adaptive Prototype Simulation (APS), a framework that reframes scalable LLM-based simulation as a recurrent oracle-allocation problem. APS retains the designated LLM as the online transition oracle while querying adaptive core prototypes, selected singleton-tail agents, and shadow-audit agents. Prototype responses induce local response surfaces for nearby agents, reducing online LLM calls without replacing the underlying transition model. To control approximation bias, shadow-audit residual correction estimates propagation residuals for aggregate correction and future budget allocation, while tail-protected singleton routing directly queries selected isolated, heterogeneous, or high-curvature regions that are vulnerable to smoothing. Theoretically, we treat APS as an estimator for full-scale high-precision individual social simulation and decompose its errors into prototype-coverage error, shadow-audit residual-correction error, local-propagation bias, and temporal context mismatch. Under the reported protocols, APS gives lower reference-aligned distributional discrepancy than scale-oriented and same-budget baselines while reducing online LLM calls, with ablations and compact robustness checks diagnosing the main bias-control mechanisms. In a 10M-agent, multi-round public-opinion simulation, APS achieves a 381.1-fold reduction over full simulation, with reference-aligned final-round JSD of 0.094 against the corresponding full-LLM reference.

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