Title: Reasoning Self-Evaluation via Trajectory Dynamics Modeling
Primary area: foundation or frontier models, including LLMs
Keywords: LRMs;CoT;Reasoning

Abstract: Large Reasoning Models (LRMs) enhance the reasoning ability of LLMs by explicitly generating chain-of-thought (CoT) trajectories before producing answers. Yet, how to enable LRMs to self-evaluate the correctness of their outputs remains largely unexplored, posing challenges for reliability. Existing approaches often rely on external supervision—such as ground-truth labels, auxiliary tools, or additional training—which largely reduces their applicability in real-world scenarios. 
In this work, we show that reasoning trajectories revealing the intermediate dynamics can be leveraged for self-evaluation, allowing models to self-assess the reliability of the reasoning without external supervision. We propose the $D^3$ Reasoning Score, a trajectory-based metric that quantifies output correctness built with three complementary components: Distance ($D_1$), Dynamism ($D_2$), and Difficulty ($D_3$), each of which can be derived purely based on internal signals of the model. Jointly modeling these factors yields a single scalar score aligned with output reliability, providing a fine-grained, white-box measure of reasoning fidelity.  
Extensive experiments across eight open-source LRMs and seven widely used benchmarks—covering both language and multimodal tasks—demonstrate that $D^3$ consistently outperforms prior baselines in AUROC, AUPR, and FPR@95. These results establish $D^3$ as a principled and practical metric for systematic self-evaluation and diagnosis of reasoning trajectories.