arXiv AI
Jun 8
CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
arXiv:2605. 28742v2 Announce Type: replace Abstract: Language models can use verifiable rewards to improve at a wide variety of reasoning tasks.
Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces
arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?
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arXiv AI
2d ago
The Metacognitive Bottleneck: Japanese Riddles Reveal Fundamental Limits of Machine Insight and Self-Evaluation in Reasoning AI
arXiv:2509. 14704v3 Announce Type: replace Abstract: Benchmark saturation and training-data contamination increasingly obscure whether reported gains in large language models (LLMs) reflect genuine advances in reasoning or familiarity with recurring patterns in benchmark problems.
arXiv AI
Jul 21
Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
arXiv:2602. 03061v2 Announce Type: replace-cross Abstract: Evaluating mathematical reasoning in LLMs is constrained by limited benchmark sizes and inherent model stochasticity, yielding high-variance accuracy estimates and unstable rankings across platforms.
arXiv AI
Jul 1
Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework
arXiv:2605. 24661v2 Announce Type: replace Abstract: LLMs have achieved remarkable success in complex reasoning tasks, yet current evaluation approaches predominantly rely on final-answer correctness, offering limited insight into the underlying reasoning processes that produce those answers.
arXiv Machine Learning
Jul 31
Reasoning Consensus: Structural Ensembling of LLM Reasoning via Weighted DAG Aggregation
arXiv:2607. 27783v1 Announce Type: cross Abstract: Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose.
arXiv Machine Learning
Jun 26
Reasoning Quality Emerges Early: Data Curation for Reasoning Models
arXiv:2606. 26797v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities in Large Language Models (LLMs).
arXiv AI
5d ago
Reasoning Jury: Multi-Model Consensus for Evaluating Reasoning Traces
arXiv:2608. 12585v1 Announce Type: new Abstract: Improving reasoning LLMs requires the ability to judge the quality of long reasoning traces for effective reasoning data curation, strong training signals during reinforcement learning, and an in-depth understanding of reasoning behaviors during model performance evaluation.
Hugging Face Trending Papers
Jun 25
Reasoning Quality Emerges Early: Data Curation for Reasoning Models
Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities in Large Language Models (LLMs). However, existing methods for curating high-quality SFT data rely heavily on strong reasoning models to filter examples based on diversity and difficulty, making the curation process costly while often yielding suboptimal data quality.
arXiv AI
Jun 3
Quantifying Faithful Confidence Expression in Large Reasoning Models
arXiv:2606. 03969v1 Announce Type: cross Abstract: Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic and (linguistically) expressed confidence--is a persistent failure mode.
arXiv AI
Jul 8
Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges
arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.
arXiv AI
Jun 6
InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning
arXiv:2603. 17310v2 Announce Type: replace Abstract: Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost.
arXiv AI
Aug 5
Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility
arXiv:2608. 04001v1 Announce Type: cross Abstract: Large language models can solve substantially harder reasoning problems with more inference-time compute.