SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
arXiv cs.AI / 4/21/2026
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Key Points
- The paper argues that automated simulator construction must match observed data distributions, which differs from generic code generation approaches.
- It identifies two key failure modes for long-horizon LLM agents—contextual drift and optimization instability—caused by mixing structural mistakes with parametric calibration errors.
- SOCIA-EVO is proposed as a dual-anchored evolutionary framework that uses a static blueprint to enforce empirical constraints and a bi-level optimization scheme to separate structural refinement from parameter calibration.
- It also introduces a self-curating Strategy Playbook that proposes and weights remedial hypotheses using Bayesian-weighted retrieval from execution feedback.
- Reportedly, SOCIA-EVO validates and discards ineffective strategies via execution feedback, achieving robust convergence and producing simulators that are statistically consistent with observational data, with code and data released on GitHub.
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