SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
Yuncheng Hua, Sion Weatherhead University of New South Wales, Mehdi Jafari, Hao Xue The Hong Kong University of Science and Technology, Flora D. Salim
Automated simulator construction requires distributional fidelity, distinguishing it from generic code generation. We identify two failure modes in long-horizon LLM agents: contextual drift and optimization instability arising from conflating structural and parametric errors. We propose SOCIA-EVO, a dual-anchored evolutionary framework. SOCIA-EVO introduces: (1) a static blueprint to enforce empirical constraints; (2) a bi-level optimization to decouple structural refinement from parameter calibration; and (3) a self-curating Strategy Playbook that manages remedial hypotheses via Bayesian-weighted retrieval. By falsifying ineffective strategies through execution feedback, SOCIA-EVO achieves robust convergence, generating simulators that are statistically consistent with observational data. SOCIA-EVO’s code and data are available here: https://github.com/cruiseresearchgroup/SOCIA/tree/evo.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表