Hugging Face Trending Papers
Reasoning Patterns for Efficient AI Coding: An Empirical Study
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arXiv AI
Oct 27
Reasoning Patterns for Efficient AI Coding: An Empirical Study
arXiv:2610.34943v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) generate long reasoning traces that expose intermediate inference behavior, offering opportunities to study and control...
arXiv Computation and Language
Aug 28
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
TRACES (Tagging Reasoning Steps for Adaptive Cost‑Efficient Early‑Stopping) is a lightweight framework that tags reasoning steps of large‑language models in real time, enabling adaptive, cost‑efficient early stopping during inference. By monitoring the types of steps generated, the method identifies when models shift their reasoning after arriving at a correct answer, allowing for interpretable stopping criteria. Experiments on mathematical reasoning benchmarks (MATH500, GSM8K, AIME) and knowledge benchmarks (MMLU, GPQA) show token reductions of 20–50% while preserving accuracy, with more conservative thresholds needed for harder tasks such as BeyondAIME and IMO AnswerBench.
arXiv Machine Learning
Jun 16
Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter
arXiv:2606. 16934v1 Announce Type: cross Abstract: Reasoning with a Code Interpreter (CI) has emerged as an effective paradigm for enhancing the reasoning capabilities of large language models (LLMs) through executable computation and iterative verification.
arXiv Computation and Language
Sep 30
Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch
arXiv:2604.15490v2 Announce Type: replace Abstract: Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical...
arXiv AI
Aug 17
Early Stopping for Large Reasoning Models via Confidence Dynamics
arXiv:2604. 04930v2 Announce Type: replace-cross Abstract: Large reasoning models rely on long chain-of-thought generation to solve complex problems, but extended reasoning often incurs substantial computational cost and can even degrade performance due to overthinking.
arXiv Computation and Language
Oct 22
Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
arXiv:2605.11388v2 Announce Type: replace Abstract: Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambigu...