LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories

2026-05-29

AI Summary

This work addresses the limitation of large language models (LLMs) in reasoning tasks, where despite having access to the full search history, their implicit trajectory representation hinders effective backtracking and reuse of past states. To overcome this, the paper introduces LinTree, a novel method that explicitly encodes a linearized search tree structure within the LLM’s reasoning trajectory, using parent pointers to clearly delineate backtracking paths. This enables the model to efficiently identify and reuse previously explored search states. Experimental results on benchmark tasks—including Blocks World, grid navigation, and Sokoban—demonstrate that LinTree significantly outperforms both implicit reasoning and heuristic-based LLM search approaches, achieving higher task success rates and improved search efficiency. These findings underscore the effectiveness of explicit structural representations in enhancing LLM reasoning capabilities.

Abstract

Large language models (LLMs) often solve reasoning problems by generating intermediate traces that explore and revise partial solutions. From a search perspective, these traces can be viewed as linearized search trees, where the model extends a partial solution, abandons it when it fails, and backtracks to try alternatives. Compared with traditional heuristic-guided search, such a policy has a potential advantage: it conditions on the whole search trace rather than only on the current local state. We first test whether LLMs utilize this advantage by comparing trace-conditioned reasoning policies against best-first search equipped with an LLM heuristic that only observes the current local state. Across three controlled reasoning environments, Blocks World, grid Navigation, and Sokoban, we find that raw access to search history alone is not enough to reliably outperform heuristic search. We then study one possible reason: in LLM reasoning traces, the underlying search tree is only implicitly represented, and when the model backtracks or switches branches, the trace does not explicitly identify which earlier search state is being revisited. We show that adding simple parent pointers to explicitly represent the linearized tree (LinTree) structure improves both task performance and search efficiency relative to implicit reasoning models and LLM-heuristic-guided search. These results suggest that search history becomes most useful when its tree structure is made explicit, motivating more structure-aware representations for LLM reasoning.

Problem

• large language models
• reasoning
• search history
• backtracking
• tree structure

Innovation

• LinTree
• structured reasoning
• search history
• large language models
• explicit tree representation