Year: 2026
Type: conference-paper
Abstract: LLM-powered systems require complex multistep decision-making abilities to solve realworld tasks, yet current planning approaches face a trade-off between the high latency of inference-time search and the limited generalization of supervised fine-tuning.To address this limitation, we introduce SGA-MCTS, a framework that casts LLM planning as nonparametric retrieval.Offline, we leverage Monte Carlo Tree Search (MCTS) to explore the solution space and distill high-fidelity trajectories into State-Goal-Action (SGA) atoms.These atoms are de-lexicalized primitives that abstract concrete entities into symbolic slots, preserving reusable causal logic while discarding domain-specific noise.Online, a retrievalaugmented agent employs a hybrid symbolicsemantic mechanism to fetch relevant SGAs and re-ground them into the current context as soft reasoning hints.Empirical results on complex benchmarks demonstrate that this paradigm enables frozen, open-weights models to match the performance of SOTA systems (e.g., GPT-5) without task-specific fine-tuning.By effectively amortizing the heavy computational cost of search, SGA-MCTS achieves System 2 reasoning depth at System 1 inference speeds, rendering autonomous planning both scalable and real-time feasible.
Institution: Ant Group (China)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166856367