CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs

2026-01-16

AI Summary

This work addresses the susceptibility of large language models to noise and structural misalignment in knowledge graph reasoning, which often leads to rigid and error-prone inference. To mitigate this, the authors propose CoG, a training-free dual-process framework inspired by the dual-system theory in cognitive science—combining intuitive and analytical reasoning. CoG employs relational blueprints to impose interpretable soft constraints that guide the reasoning process and activates an evidence-driven backtracking mechanism upon detecting failure, enabling iterative refinement. This approach allows for controllable optimization of the reasoning trajectory and achieves state-of-the-art performance across three standard benchmarks, demonstrating significant improvements in both accuracy and efficiency over existing methods.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations. While Knowledge Graphs (KGs) offer explicit grounding, existing paradigms of KG-augmented LLMs typically exhibit cognitive rigidity--applying homogeneous search strategies that render them vulnerable to instability under neighborhood noise and structural misalignment leading to reasoning stagnation. To address these challenges, we propose CoG, a training-free framework inspired by Dual-Process Theory that mimics the interplay between intuition and deliberation. First, functioning as the fast, intuitive process, the Relational Blueprint Guidance module leverages relational blueprints as interpretable soft structural constraints to rapidly stabilize the search direction against noise. Second, functioning as the prudent, analytical process, the Failure-Aware Refinement module intervenes upon encountering reasoning impasses. It triggers evidence-conditioned reflection and executes controlled backtracking to overcome reasoning stagnation. Experimental results on three benchmarks demonstrate that CoG significantly outperforms state-of-the-art approaches in both accuracy and efficiency.

Problem

• Large Language Models
• Knowledge Graphs
• Reasoning Stagnation
• Hallucinations
• Cognitive Rigidity

Innovation

• Controllable Graph Reasoning
• Relational Blueprints
• Failure-Aware Refinement
• Knowledge Graphs
• Dual-Process Theory