arXiv Machine Learning
Jun 10
Predicting Future Behaviors in Reasoning Models Enables Better Steering
arXiv:2606. 11172v1 Announce Type: new Abstract: Deployed large reasoning models (LRMs) often behave unexpectedly.
Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models
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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
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
Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
arXiv:2601. 22642v2 Announce Type: replace Abstract: Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid.
arXiv Machine Learning
Aug 11
Are Latent Reasoning Models Easily Interpretable?
arXiv:2604. 04902v2 Announce Type: replace Abstract: Latent reasoning models (LRMs) have attracted significant research interest due to their low inference cost (relative to explicit reasoning models) and theoretical ability to explore multiple reasoning paths in parallel.
arXiv AI
Jul 21
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.
arXiv AI
Sep 16
State of Thought Enables Endogenous Reasoning
arXiv:2609.16055v1 Announce Type: cross Abstract: Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasonin...
arXiv Machine Learning
Aug 11
Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond
arXiv:2510. 19990v2 Announce Type: replace Abstract: The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving.
arXiv Machine Learning
Jun 2
Are Large Reasoning Models Interruptible?
arXiv:2510. 11713v4 Announce Type: replace-cross Abstract: Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments.
arXiv AI
Jul 14
SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models
arXiv:2607. 10296v1 Announce Type: new Abstract: Reasoning failures in large language models (LLMs) are usually evaluated from final answers, but a wrong answer does not reveal why the model failed.
arXiv AI
Jul 17
Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring
arXiv:2512. 14332v2 Announce Type: replace-cross Abstract: The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately.
arXiv AI
Aug 18
A Temporal Reasoning Benchmarking Framework for LRMs via Difficulty-controlled and Dynamic Test Generation
arXiv:2607. 04784v1 Announce Type: cross Abstract: Defining the reasoning boundaries and ensuring the reliability of Large Reasoning Models (LRMs) remains a critical challenge.