{"total":126,"rows":[{"id":"2207.05221","title":"Language Models (Mostly) Know What They Know","created":"2022-07-11","cited_by_count":1856,"area":"lr"},{"id":"2305.11738","title":"CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing","created":"2023-05-19","cited_by_count":783,"area":"lr"},{"id":"2210.11610","title":"Large Language Models Can Self-Improve","created":"2022-10-20","cited_by_count":664,"area":"lr"},{"id":"2309.13007","title":"ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs","created":"2023-09-22","cited_by_count":309,"area":"lr"},{"id":"2307.01928","title":"Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners","created":"2023-07-04","cited_by_count":308,"area":"lr"},{"id":"2305.00633","title":"Self-Evaluation Guided Beam Search for Reasoning","created":"2023-05-01","cited_by_count":196,"area":"lr"},{"id":"2205.03401","title":"The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning","created":"2022-05-06","cited_by_count":169,"area":"lr"},{"id":"2302.12246","title":"Active Prompting with Chain-of-Thought for Large Language Models","created":"2023-02-23","cited_by_count":154,"area":"lr"},{"id":"2502.06233","title":"Confidence Improves Self-Consistency in LLMs","created":"2025-02-10","cited_by_count":113,"area":"lr"},{"id":"2401.13835","title":"What Large Language Models Know and What People Think They Know","created":"2024-01-24","cited_by_count":110,"area":"lr"},{"id":"2309.17249","title":"Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering","created":"2023-09-29","cited_by_count":94,"area":"lr"},{"id":"2503.00031","title":"Efficient Test-Time Scaling via Self-Calibration","created":"2025-02-25","cited_by_count":75,"area":"lr"},{"id":"2505.14489","title":"Reasoning Models Better Express Their Confidence","created":"2025-05-20","cited_by_count":65,"area":"lr"},{"id":"2501.19306","title":"SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling","created":"2025-01-31","cited_by_count":54,"area":"lr"},{"id":"2502.03671","title":"Advancing Reasoning in Large Language Models: Promising Methods and Approaches","created":"2025-02-05","cited_by_count":43,"area":"lr"},{"id":"2403.09972","title":"Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection","created":"2024-03-15","cited_by_count":41,"area":"lr"},{"id":"2305.14333","title":"Automatic Model Selection with Large Language Models for Reasoning","created":"2023-05-23","cited_by_count":35,"area":"lr"},{"id":"2609.26550","title":"JEV-as-a-Judge: Accept When Confident, Escalate When Unsure","created":"2026-09-22","cited_by_count":30,"area":"lr"},{"id":"2512.21919","title":"SWE-RM: Execution-free Feedback For Software Engineering Agents","created":"2025-12-26","cited_by_count":28,"area":"lr"},{"id":"2402.09552","title":"STEER: Assessing the Economic Rationality of Large Language Models","created":"2024-02-14","cited_by_count":27,"area":"lr"},{"id":"2502.11250","title":"Uncertainty-Aware Step-wise Verification with Generative Reward Models","created":"2025-02-16","cited_by_count":22,"area":"lr"},{"id":"2311.00445","title":"A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models","created":"2023-11-01","cited_by_count":20,"area":"lr"},{"id":"2410.10857","title":"Mirror-Consistency: Harnessing Inconsistency in Majority Voting","created":"2024-10-07","cited_by_count":17,"area":"lr"},{"id":"2412.02186","title":"VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding","created":"2024-12-03","cited_by_count":17,"area":"lr"},{"id":"2502.14634","title":"CER: Confidence Enhanced Reasoning in LLMs","created":"2025-02-20","cited_by_count":17,"area":"lr"},{"id":"2512.03109","title":"E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing","created":"2025-12-02","cited_by_count":17,"area":"lr"},{"id":"2503.08308","title":"Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework","created":"2025-03-11","cited_by_count":12,"area":"lr"},{"id":"2501.09775","title":"Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong","created":"2025-01-16","cited_by_count":11,"area":"lr"},{"id":"2602.01288","title":"EDIS: Diagnosing LLM Reasoning via Entropy Dynamics","created":"2026-02-01","cited_by_count":10,"area":"lr"},{"id":"2609.31142","title":"JevAdvBench: A Benchmark and Black-Box Attacks for Reinforcement Learning for Calibrated Decisions Models","created":"2026-09-25","cited_by_count":10,"area":"lr"},{"id":"2602.07186","title":"The Value of Variance: Mitigating Debate Collapse in Multi-Agent Systems via Uncertainty-Driven Policy Optimization","created":"2026-02-06","cited_by_count":8,"area":"lr"},{"id":"2602.11683","title":"ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces","created":"2026-02-12","cited_by_count":8,"area":"lr"},{"id":"2311.09325","title":"Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the \"right reasons\"?","created":"2023-11-15","cited_by_count":7,"area":"lr"},{"id":"2502.13119","title":"STEER-ME: Assessing the Microeconomic Reasoning of Large Language Models","created":"2025-02-18","cited_by_count":7,"area":"lr"},{"id":"2509.24816","title":"KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning","created":"2025-09-29","cited_by_count":7,"area":"lr"},{"id":"2510.09133","title":"On the Provable Performance Guarantee of Efficient Reasoning Models","created":"2025-10-10","cited_by_count":6,"area":"lr"},{"id":"2605.04039","title":"Safety and accuracy follow different scaling laws in clinical large language models","created":"2026-05-05","cited_by_count":6,"area":"lr"},{"id":"2408.08248","title":"Conformalized Answer Set Prediction for Knowledge Graph Embedding","created":"2024-08-15","cited_by_count":5,"area":"lr"},{"id":"2503.01754","title":"SDRT: Enhance Vision-Language Models by Self-Distillation with Diverse Reasoning Traces","created":"2025-03-03","cited_by_count":5,"area":"lr"},{"id":"2505.09591","title":"Variational Visual Question Answering for Uncertainty-Aware Selective Prediction","created":"2025-05-14","cited_by_count":5,"area":"lr"},{"id":"2510.05987","title":"Sample Smart, Not Hard: Correctness-First Decoding for Better Reasoning in LLMs","created":"2025-10-07","cited_by_count":5,"area":"lr"},{"id":"2510.23458","title":"BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents","created":"2025-10-27","cited_by_count":5,"area":"lr"},{"id":"2604.03696","title":"FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning","created":"2026-04-04","cited_by_count":5,"area":"lr"},{"id":"2601.09929","title":"Hallucination Detection and Mitigation in Large Language Models","created":"2026-01-14","cited_by_count":4,"area":"lr"},{"id":"2603.28135","title":"CoT2-Meta: Budgeted Metacognitive Control for Test-Time Reasoning","created":"2026-03-30","cited_by_count":4,"area":"lr"},{"id":"2502.08932","title":"On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms","created":"2025-02-13","cited_by_count":3,"area":"lr"},{"id":"2506.11166","title":"Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning","created":"2025-06-11","cited_by_count":3,"area":"lr"},{"id":"2510.27266","title":"Enhancing Trustworthy GUI Grounding via Self-Critiqued Reinforcement Learning","created":"2025-10-31","cited_by_count":3,"area":"lr"},{"id":"2601.15296","title":"Entropy-Tree: Tree-Based Decoding with Entropy-Guided Exploration","created":"2026-01-02","cited_by_count":3,"area":"lr"},{"id":"2602.19281","title":"Limited Reasoning Space: The cage of long-horizon reasoning in LLMs","created":"2026-02-22","cited_by_count":3,"area":"lr"},{"id":"2602.22751","title":"Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning","created":"2026-02-26","cited_by_count":3,"area":"lr"},{"id":"2604.19444","title":"Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation","created":"2026-04-21","cited_by_count":3,"area":"lr"},{"id":"2606.11211","title":"Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models","created":"2026-04-24","cited_by_count":3,"area":"lr"},{"id":"2608.11403","title":"When Self-Consistency Backfires: Majority Vote Hurts the Majority of Hard Science Problems for Small LLMs","created":"2026-08-11","cited_by_count":3,"area":"lr"},{"id":"2309.08969","title":"Rethinking STS and NLI in Large Language Models","created":"2023-09-16","cited_by_count":2,"area":"lr"},{"id":"2406.06863","title":"Ollabench: Evaluating LLMs' Reasoning for Human-centric Interdependent Cybersecurity","created":"2024-06-11","cited_by_count":2,"area":"lr"},{"id":"2510.21007","title":"Can Confidence Estimates Decide When Chain-of-Thought Is Necessary for LLMs?","created":"2025-10-23","cited_by_count":2,"area":"lr"},{"id":"2512.09548","title":"Supporting Dynamic Agentic Workloads: How Data and Agents Interact","created":"2025-12-10","cited_by_count":2,"area":"lr"},{"id":"2602.16154","title":"Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution","created":"2026-02-18","cited_by_count":2,"area":"lr"},{"id":"2603.00532","title":"DenoiseFlow: Uncertainty-Aware Denoising for Reliable LLM Agentic Workflows","created":"2026-02-28","cited_by_count":2,"area":"lr"},{"id":"2603.03417","title":"Parallel Test-Time Scaling with Multi-Sequence Verifiers","created":"2026-03-03","cited_by_count":2,"area":"lr"},{"id":"2604.04207","title":"Don't Blink: Evidence Collapse during Multimodal Reasoning","created":"2026-04-05","cited_by_count":2,"area":"lr"},{"id":"2604.23333","title":"Process Supervision of Confidence Margin for Calibrated LLM Reasoning","created":"2026-04-25","cited_by_count":2,"area":"lr"},{"id":"2605.30557","title":"Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?","created":"2026-05-28","cited_by_count":2,"area":"lr"},{"id":"2607.06799","title":"What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study","created":"2026-07-07","cited_by_count":2,"area":"lr"},{"id":"2507.10007","title":"Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning","created":"2025-07-14","cited_by_count":1,"area":"lr"},{"id":"2508.04576","title":"ConfProBench: A Confidence Evaluation Benchmark for MLLM-Based Process Judges","created":"2025-08-06","cited_by_count":1,"area":"lr"},{"id":"2511.02197","title":"Open the Oyster: Empirical Evaluation and Improvement of Code Reasoning Confidence in LLMs","created":"2025-11-04","cited_by_count":1,"area":"lr"},{"id":"2511.11916","title":"An Analysis of Architectural Impact on LLM-based Abstract Visual Reasoning: A Systematic Benchmark on RAVEN-FAIR","created":"2025-11-14","cited_by_count":1,"area":"lr"},{"id":"2512.15033","title":"Beyond Accuracy: A Geometric Stability Analysis of Large Language Models in Chess Evaluation","created":"2025-12-17","cited_by_count":1,"area":"lr"},{"id":"2602.02550","title":"HyPAC: Cost-Efficient LLMs-Human Hybrid Annotation with PAC Error Guarantees","created":"2026-01-30","cited_by_count":1,"area":"lr"},{"id":"2602.02863","title":"\"I May Not Have Articulated Myself Clearly\": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time","created":"2026-02-02","cited_by_count":1,"area":"lr"},{"id":"2602.08520","title":"Reinforcement Inference: Leveraging Uncertainty for Self-Correcting Language Model Reasoning","created":"2026-02-09","cited_by_count":1,"area":"lr"},{"id":"2603.06317","title":"From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty","created":"2026-03-06","cited_by_count":1,"area":"lr"},{"id":"2603.19118","title":"How Uncertainty Estimation Scales with Sampling in Reasoning Models","created":"2026-03-19","cited_by_count":1,"area":"lr"},{"id":"2603.19262","title":"Empirical Characterization of Inference-Time Elicited Probability Transformations in Large Language Models","created":"2026-02-26","cited_by_count":1,"area":"lr"},{"id":"2605.16999","title":"Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning","created":"2026-05-16","cited_by_count":1,"area":"lr"},{"id":"2606.13156","title":"Iterative Visual Thinking and the Self-Correction Mirage in VLM Grounding","created":"2026-06-11","cited_by_count":1,"area":"lr"},{"id":"2606.19808","title":"Think Again or Think Longer? Selective Verification for Budget-Aware Reasoning","created":"2026-06-18","cited_by_count":1,"area":"lr"},{"id":"2606.24281","title":"CALIBER: Calibrating Confidence Before and After Reasoning in Language Models","created":"2026-06-23","cited_by_count":1,"area":"lr"},{"id":"2606.29654","title":"Budgeted Act-or-Defer Multi-Agent LLM Deliberation with Local Reliability Bounds","created":"2026-06-28","cited_by_count":1,"area":"lr"},{"id":"2607.13753","title":"Post-Training Shifts Confidence: A Three-Stage Analysis of How SFT, RL, and OPD Shape CoT Calibration","created":"2026-07-15","cited_by_count":1,"area":"lr"},{"id":"2407.03004","title":"SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy","created":"2024-07-03","cited_by_count":0,"area":"lr"},{"id":"2508.19376","title":"Fine-Tuning Vision-Language Models for Neutrino Event Analysis in High-Energy Physics Experiments","created":"2025-08-26","cited_by_count":0,"area":"lr"},{"id":"2509.13262","title":"Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning","created":"2025-09-16","cited_by_count":0,"area":"lr"},{"id":"2510.22909","title":"Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions","created":"2025-10-27","cited_by_count":0,"area":"lr"},{"id":"2602.06446","title":"CORE: Comprehensive Ontological Relation Evaluation for Large Language Models","created":"2026-02-06","cited_by_count":0,"area":"lr"},{"id":"2602.15867","title":"Playing With AI: How Do State-Of-The-Art Large Language Models Perform in the 1977 Text-Based Adventure Game Zork?","created":"2026-01-27","cited_by_count":0,"area":"lr"},{"id":"2603.17811","title":"Dropout Robustness and Cognitive Profiling of Transformer Models via Stochastic Inference","created":"2026-03-18","cited_by_count":0,"area":"lr"},{"id":"2604.05757","title":"Identifying Influential N-grams in Confidence Calibration via Regression Analysis","created":"2026-04-07","cited_by_count":0,"area":"lr"},{"id":"2605.06785","title":"Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport","created":"2026-05-07","cited_by_count":0,"area":"lr"},{"id":"2605.08452","title":"NICE FACT: Diagnosing and Calibrating VLMs in Quantitative Reasoning for Kinematic Physics","created":"2026-05-08","cited_by_count":0,"area":"lr"},{"id":"2605.25566","title":"Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis","created":"2026-05-25","cited_by_count":0,"area":"lr"},{"id":"2606.04435","title":"Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation","created":"2026-06-03","cited_by_count":0,"area":"lr"},{"id":"2606.09030","title":"TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs","created":"2026-06-08","cited_by_count":0,"area":"lr"},{"id":"2606.13591","title":"Confidence Composition for Multiagent Language Model Systems","created":"2026-06-11","cited_by_count":0,"area":"lr"},{"id":"2606.16811","title":"Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier","created":"2026-06-15","cited_by_count":0,"area":"lr"},{"id":"2606.19812","title":"Human-on-the-Loop Orchestration for AI-Assisted Legal Discovery","created":"2026-06-18","cited_by_count":0,"area":"lr"},{"id":"2606.21083","title":"Coherence Under Commitment: Probing Generalization and Vacuous Memorization in LLM Logical Reasoning","created":"2026-06-19","cited_by_count":0,"area":"lr"},{"id":"2607.08017","title":"Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework","created":"2026-07-09","cited_by_count":0,"area":"lr"}],"next_cursor":"100"}