Technology Category
Search and Optimization: Sampling/Simulation-based SearchMultiagent Systems: Agent-Based Simulation and Emergent BehaviorHumans and AI: Human-Aware Planning and Behavior Prediction
SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
🤖 AI Summary
This work proposes the SOCIA-EVO framework to address optimization instability in long-horizon large language model agents during automated simulator construction, a challenge arising from context drift and entanglement between structural and parametric errors. The approach enforces structural plausibility through static blueprints and decouples structure search from parameter calibration via bilevel optimization. Additionally, it incorporates a self-maintained strategy playbook that dynamically prunes ineffective strategies by integrating Bayesian-weighted retrieval with execution feedback. This enables hypothesis-driven evolution of simulators whose statistical distributions align closely with real-world observational data, substantially enhancing the stability, fidelity, and verifiability of long-horizon simulations.
Technology Category
Application Category
📝 Abstract
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. The code and data of SOCIA-EVO are available here: https://github.com/cruiseresearchgroup/SOCIA/tree/evo.
Problem
Research questions and friction points this paper is trying to address.
automated simulator construction
distributional fidelity
contextual drift
optimization instability
structural and parametric errors
Innovation
Methods, ideas, or system contributions that make the work stand out.
dual-anchored optimization
bi-level optimization
simulator construction
Bayesian-weighted retrieval
distributional fidelity
🔎 Similar Papers
No similar papers found.
