TRACS: Combining LLM-Generated Plan Trajectories with Classical Search for Numeric Planning

Numeric planning requires long-horizon reasoning under logical and numerical constraints. Advances in model architecture and increases in model scale have strengthened the planning capabilities of frontier large language models (LLMs), but a single inapplicable action can invalidate an entire generated plan. We introduce TRACS, a solver-centered hybrid that exploits LLM planning capability without requiring the model to solve each problem independently. Stagnation signals trigger occasional requests for LLM continuations from selected states. Symbolic replay retains valid prefixes for integration into native eager or lazy search. Symbolic validation preserves plan validity, while bounded intervention preserves the backbone's conditional completeness. We instantiate TRACS with a 9B proposer trained on solver-verified residual continuations using supervised fine-tuning followed by policy optimization, rewarding executable progress and penalizing state revisits. Across Depots-Num and ZenoTravel-Num, the lazy-search variant of TRACS achieves full coverage and substantially improves solution quality over native planners under the reported deployments.