arXiv AI July 17, 2026 By Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, John D. Kelleher

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.

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benchmarks

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.

By Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, John D. Kelleher

benchmarks

arXiv AI

Jun 6

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

arXiv:2606. 05402v1 Announce Type: cross Abstract: Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluation and monitoring of the reasoning process.

By Jinu Lee, Shivam Agarwal, Amruta Parulekar, Siddarth Madala, Dilek Hakkani-Tur, Julia Hockenmaier

llms

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.

By Connor Dilgren, Sarah Wiegreffe

benchmarks safety

arXiv AI

Jul 28

Reason Popper-ly: Patching In-Context Reasoning with Inductive Logic Programming

arXiv:2607. 23019v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting enables large language models (LLMs) to tackle multi-step reasoning tasks, yet the generated intermediate steps are not guaranteed to be logically sound.

By Zirong Chen, Meiyi Ma

llms benchmarks

arXiv Computation and Language

Aug 24

Self-Speculation for Faster Reasoning Models

arXiv:2608.20359v1 Announce Type: new Abstract: Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performanc...

By Ravisri Valluri, Tung Nguyen, Aditya Grover

llms agents

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.

By Evgenii Kortukov, Piotr Komorowski, Florian Klein, Paula Engl, Gabriele Sarti, Seong Joon Oh, Sebastian Lapuschkin, Wojciech Samek