SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
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 achiev...
| Authors | Yuncheng Hua, Sion Weatherhead, Mehdi Jafari et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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