arXiv AI

eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion

arXiv:2606. 02054v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve impressive performance on multi-step reasoning tasks, their reliability is persistently hindered by critical limitations such as unconstrained hallucinations and poor numerical computation.

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 AI

Jul 8

Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges

arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.

By Husnain Amjad, Raja Khurram Shahzad, Aamir Shahzad, Mehwish Fatima

llms fine-tuning benchmarks

arXiv AI

Jul 29

Penelope: Localized Latent Recurrence for Efficient Structured Reasoning

arXiv:2607. 25915v1 Announce Type: new Abstract: Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or by serializing intermediate steps as chain-of-thought (CoT) tokens.

By Yutong Chen, Shouqian Shi, Xinran Liu, Haochen Wang, Jiaying Wang, Tianxing Xu, Yuanxi Wang, Zirui Ding

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jun 17

Continual Self-Improvement with Lightweight Experiential Latent Memories

arXiv:2606. 17803v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning traces generated during this process.

By Vaggelis Dorovatas, Nancy Kalaj, Rahaf Aljundi

llms reinforcement-learning benchmarks

arXiv AI

Jun 9

MixReasoning: Switching Modes to Think

arXiv:2510. 06052v2 Announce Type: replace Abstract: Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of thought before producing an answer.

By Haiquan Lu, Gongfan Fang, Xinyin Ma, Qi Li, Xinchao Wang

arXiv AI

Jul 22

Fluid Reasoning Representations

arXiv:2602. 04843v2 Announce Type: replace Abstract: Frontier large language models increasingly solve complex tasks involving abstract concepts through extended test-time thinking.

By Dmitrii Kharlapenko, Terry Jingchen Zhang, Arth Singh, Alessandro Stolfo, Arthur Conmy, Mrinmaya Sachan, Zhijing Jin

llms

arXiv AI

Jun 15

Fractured Chain-of-Thought Reasoning

arXiv:2505. 12992v4 Announce Type: replace-cross Abstract: Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining.

By Baohao Liao, Hanze Dong, Yuhui Xu, Doyen Sahoo, Christof Monz, Junnan Li, Caiming Xiong

llms benchmarks

arXiv AI

Jun 11

GPO: Learning from Critical Steps to Improve LLM Reasoning

arXiv:2509. 16456v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks.

By Jiahao Yu, Zelei Cheng, Xian Wu, Xinyu Xing

llms fine-tuning benchmarks

arXiv AI

Jun 8

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

arXiv:2606. 06915v1 Announce Type: cross Abstract: Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.

By Vladislav Smirnov (MBZUAI), Chieu Nguyen (MBZUAI), Sergey Senichev (Independent Researcher), Minh Ngoc Ta (MBZUAI), Ekaterina Fadeeva (ETH Z\"urich), Artem Vazhentsev (MBZUAI), Daria Galimzianova (MBZUAI), Nikolai Rozanov (MBZUAI, Imperial College London), Viktor Mazanov (Innopolis University), Jingwei Ni (ETH Z\"urich), Tianyi Wu (NUS), Igor Kiselev (Accenture), Mrinmaya Sachan (ETH Z\"urich), Iryna Gurevych (MBZUAI), Preslav Nakov (MBZUAI), Timothy Baldwin (MBZUAI), Artem Shelmanov (MBZUAI)

llms rag benchmarks

arXiv Machine Learning

Jun 25

SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

arXiv:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.

By Xiuwei Chen, Wentao Hu, Hanhui Li, Yongxin Wang Jun Zhou, Zisheng Chen, Meng Cao, Yihan Zeng, Kui Zhang, Yu-Jie Yuan, Jianhua Han, Hang Xu, Xiaodan Liang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jul 14

MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models

arXiv:2603. 01331v3 Announce Type: replace-cross Abstract: Discrete diffusion language models (dLLMs) generate text by iteratively denoising a masked sequence.

By Kejing Xia, Mingzhe Li, Lixuan Wei, Zhenbang Du, Xiangchi Yuan, Dachuan Shi, Qirui Jin, Wenke Lee

llms diffusion benchmarks

arXiv AI

Jun 16

When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions

arXiv:2605. 22873v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning has become the default strategy for enhancing LLM capabilities, yet its application raises a fundamental question: when is explicit reasoning actually beneficial?

By Wei Xia, Haoqing Wang, Zhi-Hong Deng, Yehui Tang

llms benchmarks

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