arXiv:2606. 03883v1 Announce Type: new Abstract: Large reasoning models (LRMs) are often evaluated using metrics such as final-answer accuracy or token count.
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arXiv:2606. 20227v1 Announce Type: new Abstract: Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making.
arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?
The paper investigates when intermediate reasoning traces benefit machine translation by examining models, languages, domains, and datasets. It finds that the optimal reasoning language depends on the model, reasoning length has a non‑monotonic effect on quality, and traces display recurring functional patterns. Using Hierarchical Meta‑Summarization, the authors uncover a shared structure of understanding/planning, translating/drafting, and refining/verifying, while also noting domain‑specific variations, suggesting that reasoning should be tailored rather than uniformly applied.
arXiv:2609.21492v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely r...
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: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.