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This paper proposes a two-stage physics-constrained autoregressive transformer model to predict driver stop-go decisions and longitudinal trajectories at signalized intersections, using real-world data to enhance traffic safety.
This paper proposes SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents in decentralized energy markets, integrating an LLM-based Planner/Auditor layer and revealing a utility-safety trade-off.
Proposes SNAP-FM, a method that leverages sparse GPU nonlinear optimization to accelerate constraint projection in physics-constrained generative modeling, achieving faster inference while preserving exact physical constraint satisfaction.