TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models

• URL: http://arxiv.org/abs/2604.04942v1
• Date: Fri, 13 Mar 2026 13:01:01 GMT
• Title: TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models
• Abstract summary: Chain-of-Thought (CoT) paradigm dominates practical applications for its single-round efficiency, yet its reasoning chains often exhibit logical gaps.<n>This paper proposes a topology-based method for optimizing reasoning chains.<n>We show that our approach offers a superior balance between reasoning accuracy and efficiency, showcasing a practical solution to single-round generation with multi-round intelligence''
• Score: 54.20787536978013
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Enhancing the reasoning capability of large language models (LLMs) remains a core challenge in natural language processing. The Chain-of-Thought (CoT) paradigm dominates practical applications for its single-round efficiency, yet its reasoning chains often exhibit logical gaps. While multi-round paradigms like Graph-of-Thoughts (GoT), Tree-of-Thoughts (ToT), and Atom of Thought (AoT) achieve strong performance and reveal effective reasoning structures, their high cost limits practical use. To address this problem, this paper proposes a topology-based method for optimizing reasoning chains. The framework embeds essential topological patterns of effective reasoning into the lightweight CoT paradigm. Using persistent homology, we map CoT, ToT, and GoT into a unified topological space to quantify their structural features. On this basis, we design a unified optimization system: a Topological Optimization Agent diagnoses deviations in CoT chains from desirable topological characteristics and simultaneously generates targeted strategies to repair these structural deficiencies. Compared with multi-round reasoning methods like ToT and GoT, experiments on multiple datasets show that our approach offers a superior balance between reasoning accuracy and efficiency, showcasing a practical solution to ``single-round generation with multi-round intelligence''.

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