New research probes LLM reasoning, instruction following, and self-correction

COVERAGE [8]

1. arXiv cs.AI TIER_1 English(EN) · Junyao Yang, Chen Qian, Kun Wang, Linfeng Zhang, Quanshi Zhang, Yong Liu, Dongrui Liu · 2026-05-25 04:00

   Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models

   arXiv:2605.17770v2 Announce Type: replace Abstract: The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-step ``slow thinking'' reasoning, unlocking state-of-the-art performance in c…

2. arXiv cs.CL TIER_1 English(EN) · Zhe Yuan, Yipeng Zhou, Jinghan Li, Xinyuan Chen, Bowen Deng, Zhiqian Chen, Liang Zhao · 2026-05-25 04:00

   LambdaPO: A Lambda Style Policy Optimization for Reasoning Language Models

   arXiv:2605.19416v2 Announce Type: replace Abstract: Group Relative Policy Optimization(GRPO) has become a cornerstone of modern reinforcement learning alignment, prized for its efficacy in foregoing an explicit value-critic by leveraging reward normalization across sampled trajec…

3. arXiv cs.LG TIER_1 English(EN) · Meir Roketlishvili, Semyon Semenov, Maksim Bobrin, Viktor Kovalchuk, Albert Baichorov, Abduragim Shtanchaev, Fakhri Karray, Dmitry V. Dylov, Martin Tak\'a\v{c}, Arip Asadulaev · 2026-05-25 04:00

   Convex Compositional Reasoning Models

   arXiv:2605.23395v1 Announce Type: new Abstract: Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constraints. In our paper, we show that a key bottleneck in compositional reasoning is …

4. arXiv cs.LG TIER_1 English(EN) · Hoang Phan, Quang H. Nguyen, Hung T. Q. Le, Xiusi Chen, Heng Ji, Khoa D. Doan · 2026-05-25 04:00

   Decoding the Critique Mechanism in Large Reasoning Models

   arXiv:2603.16331v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong performance on complex logical benchmarks. We hypothe…

5. arXiv cs.LG TIER_1 English(EN) · Arip Asadulaev · 2026-05-22 09:04

   Convex Compositional Reasoning Models

   Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constraints. In our paper, we show that a key bottleneck in compositional reasoning is not composition itself, but the non-convex geome…

6. Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-20 00:00

   Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning

   Equilibrium Reasoners enable scalable reasoning through task-conditioned attractors that guide latent dynamical systems toward valid solutions, achieving significant accuracy improvements through iterative test-time computation.

7. Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-19 06:10

   LambdaPO: A Lambda Style Policy Optimization for Reasoning Language Models

   Group Relative Policy Optimization(GRPO) has become a cornerstone of modern reinforcement learning alignment, prized for its efficacy in foregoing an explicit value-critic by leveraging reward normalization across sampled trajectory cohorts. However, the method's reliance on a mo…

8. Together AI blog TIER_1 English(EN) · 2025-10-22 00:00

   Large Reasoning Models Fail to Follow Instructions During Reasoning: A Benchmark Study

   ReasonIF finds frontier LRMs fail to follow reasoning instructions >75% of the time; introduces a benchmark across languages, formatting, and length.