Year: 2026
Type: conference-paper
Abstract: Large language models have shown strong reasoning capabilities through chain-structured methods such as Chain-of-Thought.Recent studies optimize thought structures by generating parallel or tree-like structures, switching long and short reasoning modes, or aligning reasoning steps with task performance.However, these approaches mainly rely on previously generated logical directions of the chains, which ignore the unexplored regions of the solution space.Such a phenomenon is denoted as blind spots, which limit the diversity and effectiveness of the reasoning process.To this end, we propose the "Thought Space Explorer" (TSE), a framework for navigating and expanding thought structures to overcome blind spots in LLM reasoning.Our TSE first identifies key nodes with high impact, then generates new nodes by integrating information from multiple chains.Finally, it extends new branches through connection strategies.We conduct a series of experiments on math and QA benchmarks.Compared to existing baseline methods, TSE improves the accuracy of both the final answer and intermediate reasoning steps, while maintaining a better effectiveness-efficiency trade-off for practical deployment.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
Awards: Collaborative Research: III: Small: Closing Sim-to-Real Gap in Reinforcement Learning via Randomization, Alignment, and Derivation, Collaborative Research: III: Small: Advancing Data-Centric AI through Generative Approaches for Feature Space Reconstruction, III: Small: Deep Interactive Reinforcement Learning for Self-optimizing Feature Selection, CRII: III: Towards Traceable, Affordable and Explainable Automated Feature Generation for Tabular Data, Collaborative Research: III: Small: Closing Sim-to-Real Gap in Reinforcement Learning via Randomization, Alignment, and Derivation, RII Track-1: AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina, CAREER: Reinforced Imitative Graph Learning: Bridging the Gap between Perception and Prescription in Graph Sequences
OpenAlex ID: W7140144297