arXiv AI August 11, 2026 By Jiayi Tian, Yupeng Su, Ryan Solgi, Souvik Kundu, Zheng Zhang

RankGuide: Tensor-Rank-Guided Routing and Steering for Efficient Reasoning

arXiv:2604. 16694v2 Announce Type: replace Abstract: Large reasoning models (LRMs) enhance problem-solving capabilities by generating explicit multi-step chains of thought (CoT) reasoning; however, they incur substantial inference latency and computational overhead.

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efficiency

arXiv Computation and Language

3d ago

Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

arXiv:2609.39346v1 Announce Type: new Abstract: Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (S...

By Bohan Zhang (Southeast University), Linan Yue (Southeast University), Weibo Gao (Hong Kong Polytechnic University), Pengyu Chen (Southeast University), Hong Guo (Southeast University), Yanqi Hao (ZTE Corporation)

llms efficiency benchmarks

arXiv AI

Jun 8

DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling

arXiv:2606. 07108v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing complex tasks, yet suffer from inefficiencies due to redundant reasoning, known as "overthinking".

By Tengyao Tu, Yulin Li, Hui-Ling Zhen, Libo Qin, Zhoujun Wei, Jinghua Piao, Zhuotao Tian, Yong Li, Min Zhang

rag nlp benchmarks

arXiv Machine Learning

Jun 9

ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning

arXiv:2601. 03093v2 Announce Type: replace Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (LLMs) toward improved reasoning and efficiency without updating model parameters.

By Tuc Nguyen, Thai Le

llms reinforcement-learning benchmarks

arXiv AI

Aug 25

SAEM: Stage-Aware Expert Management for Memory-Efficient MoE Inference in Chain-of-Thought Reasoning

arXiv:2608.21614v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting improves LLM reasoning by decomposing complex problems into intermediate steps, but its sequential nature increases de...

By Yujie Zhang, Bin Gao, Tulika Mitra

llms efficiency benchmarks

arXiv AI

Jun 9

Generative Reasoning Re-ranker

arXiv:2602. 07774v5 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge.

By Mingfu Liang, Yufei Li, Jay Xu, Kavosh Asadi, Xi Liu, Shuo Gu, Kaushik Rangadurai, Frank Shyu, Shuaiwen Wang, Song Yang, Zhijing Li, Jiang Liu, Mengying Sun, Fei Tian, Xiaohan Wei, Chonglin Sun, Jacob Tao, Shike Mei, Wenlin Chen, Santanu Kolay, Sandeep Pandey, Hamed Firooz, Luke Simon

llms rag nlp reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 5

Latent Reasoning with Normalizing Flows

arXiv:2606. 06447v1 Announce Type: cross Abstract: Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation.

By Guancheng Tu, Xiangjun Fu, Suhao Yu, Yao Tang, Haoqiang Kang, Lianhui Qin, Yizhe Zhang, Jiatao Gu

llms efficiency benchmarks

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Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling

ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning

SAEM: Stage-Aware Expert Management for Memory-Efficient MoE Inference in Chain-of-Thought Reasoning

Generative Reasoning Re-ranker

Latent Reasoning with Normalizing Flows