arXiv AI June 4, 2026 By Jo\~ao Pedro Gandarela, Thiago Rios, Stefan Menzel, Andr\'e Freitas

R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search

arXiv:2606. 04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizons, fluency does not ensure reliable delivery.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

1d ago

SEVerA: Verified Synthesis of Self-Evolving Agents

arXiv:2603.25111v3 Announce Type: replace Abstract: Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm...

By Debangshu Banerjee, Changming Xu, Eugene Ie, Ming Zhang, Daiyi Peng, Chu-Cheng Lin, Tarun Suresh, Gagandeep Singh

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jul 9

UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma

arXiv:2607. 06987v1 Announce Type: new Abstract: Reinforcement learning (RL) has become the standard paradigm for enhancing the complex reasoning capabilities of large language models (LLMs).

By Chongyu Fan, Pengfei Liu, Jingjia Huang, Sijia Liu, Yi Lin

llms reinforcement-learning multimodal

arXiv Machine Learning

Jul 31

LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger

arXiv:2607. 28374v1 Announce Type: new Abstract: Multimodal agents for visual question answering increasingly operate as multi-step trajectories that interleave perception, retrieval, and reasoning, yet evaluation still largely reduces to final-answer accuracy.

By Enjun Du, Hange Zhou, Chenxu Du, Siyi Liu, Zirong Chen, Ziyu Zheng, Yongqi Zhang

agents nlp multimodal benchmarks safety

arXiv AI

Sep 30

MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller

arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not...

By Zhibin Wen, Tao Han, Lei Bai, Can Li, Yang Xu

llms reinforcement-learning benchmarks

arXiv Machine Learning

Sep 23

CoEvo: Oracle-Grounded Self-Evolution of a Single Model for Multi-Step Causal Reasoning

arXiv:2609.26094v1 Announce Type: new Abstract: Multi-step causal reasoning requires chaining inferences where each step constrains the next. An early error propagates silently, and a correct answer...

By Jian Zhang, Bingyi Wang, Yizhi Liu

llms efficiency benchmarks safety

arXiv AI

Jul 21

Constraint-Anchored Reasoning Traces

arXiv:2607. 16727v1 Announce Type: new Abstract: Autoregressive multimodal large language models (MLLMs) suffer from error snowballing: a single incorrect inference early in a chainof-thought (CoT) trace corrupts all downstream reasoning.

By Zehua Cheng, Wei Dai, Jiahao Sun

llms fine-tuning multimodal benchmarks

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