{"total":29,"rows":[{"id":"2510.04040","title":"FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning","created":"2025-10-05","cited_by_count":26,"area":"lr"},{"id":"2510.10103","title":"Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning","created":"2025-10-11","cited_by_count":16,"area":"lr"},{"id":"2508.16861","title":"Learning from Diverse Reasoning Paths with Routing and Collaboration","created":"2025-08-23","cited_by_count":12,"area":"lr"},{"id":"2510.08308","title":"First Try Matters: Revisiting the Role of Reflection in Reasoning Models","created":"2025-10-09","cited_by_count":12,"area":"lr"},{"id":"2509.21933","title":"Why Chain of Thought Fails in Clinical Text Understanding","created":"2025-09-26","cited_by_count":9,"area":"lr"},{"id":"2604.08401","title":"Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing","created":"2026-04-09","cited_by_count":7,"area":"lr"},{"id":"2601.03154","title":"Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective","created":"2026-01-06","cited_by_count":6,"area":"lr"},{"id":"2510.15990","title":"Can GRPO Help LLMs Transcend Their Pretraining Origin?","created":"2025-10-14","cited_by_count":5,"area":"lr"},{"id":"2601.04992","title":"Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization","created":"2026-01-08","cited_by_count":5,"area":"lr"},{"id":"2601.11517","title":"Do explanations generalize across large reasoning models?","created":"2026-01-16","cited_by_count":5,"area":"lr"},{"id":"2510.02204","title":"Say One Thing, Do Another? Diagnosing Reasoning-Execution Gaps in VLM-Powered Mobile-Use Agents","created":"2025-10-02","cited_by_count":4,"area":"lr"},{"id":"2511.15613","title":"When to Think and When to Look: Uncertainty-Guided Lookback","created":"2025-11-19","cited_by_count":3,"area":"lr"},{"id":"2601.06160","title":"Student Guides Teacher: Weak-to-Strong Inference via Spectral Orthogonal Exploration","created":"2026-01-06","cited_by_count":3,"area":"lr"},{"id":"2605.25603","title":"Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy","created":"2026-05-25","cited_by_count":3,"area":"lr"},{"id":"2512.09679","title":"Understanding Chain-of-Thought Effectiveness in Code Generation: An Empirical and Information-Theoretic Analysis","created":"2025-12-10","cited_by_count":2,"area":"lr"},{"id":"2601.21214","title":"Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models","created":"2026-01-29","cited_by_count":1,"area":"lr"},{"id":"2602.14404","title":"Boule or Baguette? A Study on Task Topology, Length Generalization, and the Benefit of Reasoning Traces","created":"2026-02-16","cited_by_count":1,"area":"lr"},{"id":"2602.15460","title":"On the Out-of-Distribution Generalization of Reasoning in Multimodal LLMs for Simple Visual Planning Tasks","created":"2026-02-17","cited_by_count":1,"area":"lr"},{"id":"2603.19266","title":"Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion","created":"2026-02-26","cited_by_count":1,"area":"lr"},{"id":"2604.01896","title":"Bayesian Elicitation with LLMs: Model Size Helps, Extra \"Reasoning\" Doesn't Always","created":"2026-04-02","cited_by_count":1,"area":"lr"},{"id":"2510.13850","title":"Revisiting the UID Hypothesis in LLM Reasoning Traces","created":"2025-10-11","cited_by_count":0,"area":"lr"},{"id":"2510.25791","title":"The Kinetics of Reasoning: How Chain-of-Thought Shapes Learning in Transformers?","created":"2025-10-28","cited_by_count":0,"area":"lr"},{"id":"2512.05943","title":"TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models","created":"2025-12-05","cited_by_count":0,"area":"lr"},{"id":"2602.06527","title":"HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction","created":"2026-02-06","cited_by_count":0,"area":"lr"},{"id":"2604.02230","title":"Answering the Wrong Question: Reasoning Trace Inversion for Abstention in LLMs","created":"2026-04-02","cited_by_count":0,"area":"lr"},{"id":"2605.09678","title":"Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities","created":"2026-05-10","cited_by_count":0,"area":"lr"},{"id":"2605.19944","title":"A Measure-Theoretic Analysis of Reasoning: Structural Generalization and Approximation Limits","created":"2026-05-19","cited_by_count":0,"area":"lr"},{"id":"2606.11195","title":"From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning","created":"2026-04-17","cited_by_count":0,"area":"lr"},{"id":"2607.11615","title":"ThinkLog: Leveraging Reasoning for Log Statement Generation","created":"2026-07-13","cited_by_count":0,"area":"lr"}],"next_cursor":null}