TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models
Summary: arXiv:2604.04942v1 Announce Type: cross
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.
Introduction
The ability of large language models to reason effectively has become increasingly important as these models are applied across various domains. Traditional methods such as the Chain-of-Thought (CoT) approach have proven effective but are not without their limitations. The logical gaps that often appear in CoT reasoning chains highlight the need for a more robust solution.
Challenges in Current Paradigms
While multi-round reasoning paradigms such as Graph-of-Thought (GoT), Tree-of-Thought (ToT), and Atom of Thought (AoT) have demonstrated enhanced performance, the associated computational costs significantly hinder their practicality. This has created a demand for innovative methods that can bridge the gap between efficiency and reasoning quality.
Proposed Solution: Topology-Based Optimization
The authors present a novel topology-based framework designed to optimize reasoning chains by embedding essential topological patterns of effective reasoning within the lightweight CoT paradigm. This approach leverages persistent homology to map CoT, ToT, and GoT into a unified topological space, allowing for the quantification of their structural features.
Key Features of the Framework
- Topological Optimization Agent: This agent diagnoses deviations in CoT chains from desirable topological characteristics and generates targeted strategies to repair these structural deficiencies.
- Unified Optimization System: By creating a harmonious framework that incorporates the strengths of various reasoning paradigms, the proposed system offers a balanced approach to reasoning accuracy and efficiency.
- Quantitative Evaluation: The use of persistent homology provides a rigorous method for evaluating the structural integrity of reasoning chains.
Experimental Results
Experiments conducted on multiple datasets indicate that the proposed TDA-RC framework outperforms traditional multi-round reasoning methods like ToT and GoT. The results demonstrate a superior balance between reasoning accuracy and efficiency, establishing a practical solution that embodies the concept of “single-round generation with multi-round intelligence.”
Conclusion
The TDA-RC framework presents a promising advancement in the realm of large language models, offering a practical approach to enhancing reasoning capabilities. By leveraging topological patterns and optimizing the Chain-of-Thought paradigm, this research paves the way for more effective and efficient reasoning in natural language processing applications.