arXiv:2608. 20055v1 Announce Type: cross Abstract: Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets.
By Yiting Qu, Ziqing Yang, Chi Cui, Ye Leng, Junjie Chu, Yang Zhang
arXiv:2606. 14935v1 Announce Type: new Abstract: Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly.
By Agnieszka Mensfelt, Adarsh Prabhakaran, Adrian Haret, Vince Trencsenyi, Kostas Stathis
TAC-CoT is a framework that replaces explicit chain-of-thought reasoning with compact latent reasoning segments, keeping only textual tokens for tool calls, observations, and final answers. This interleaved design allows large language model agents to use tools without generating full verbal reasoning steps. Experiments on math and QA benchmarks show up to 8.8‑point accuracy gains and a 6.8× reduction in model forward passes, while also improving tool‑use quality in single- and multi‑tool settings.
By Juseon Do, Sangmin Bae, Jeesu Jung, Hwanjun Song
The paper demonstrates that frontier language models can be prompted to expose their internal chain-of-thought reasoning via a simple custom tool. By comparing these extracted traces to native reasoning on open-source models, the authors confirm that the externalized reasoning aligns with genuine reasoning and outperforms no-reasoning baselines across math, science, and code tasks. They further analyze the structure of the reasoning, noting token-efficient, directed reasoning in models like GPT‑6 Astra, which externalizes only crucial steps while handling elementary ones internally.
By Xiaoyu Luo, Tao Ren, Wenrui Yu, Xiao Li, Qiongxiu Li, Johannes Bjerva
ReasoningShield introduces a benchmark and lightweight models for detecting unsafe content in the intermediate reasoning steps of Large Reasoning Models (LRMs). The benchmark contains 9.2K annotated (Query, CoT) pairs across 10 risk categories, 3 safety levels, and 8 LRMs, and the proposed models achieve 91.8% F1, outperforming existing moderation tools by significant margins. The study highlights that current moderation tools degrade by up to 35.2% on Chain-of-Thought reasoning compared to traditional answer moderation.
By Changyi Li, Jiayi Wang, Xudong Pan, Geng Hong, Min Yang
arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.
By Shashwat Sourav, Aishwarya Balwani