arXiv AI August 10, 2026 By Fouad Bahrpeyma, Dirk Reichelt

A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing

arXiv:2608. 07148v1 Announce Type: new Abstract: Modern manufacturing imposes six coupled demands on adaptive control: local decisions with global consequences, partial observability, nonstationarity, reflex speed response with long horizon effects, delayed and diffuse outcomes, and dynamics that resist explicit modeling.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 8

Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

arXiv:2607. 05458v1 Announce Type: cross Abstract: Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure.

By Haiwen Yi, Xinyuan Song

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 9

Benchmarking Open-Ended Multi-Agent Coordination in Language Agents

arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.

By Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker, Alexander Rutherford, Davide Paglieri, Aidan Scannell, Henry Gouk, Elliot J. Crowley, Tim Rockt\"aschel, Amos Storkey

llms agents benchmarks

Hugging Face Trending Papers

Jul 5

Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure. We argue that this harness is itself a learnable control layer.

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 2

MARFT: Multi-Agent Reinforcement Fine-Tuning

arXiv:2504. 16129v5 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) have demonstrated strong capabilities on complex agentic tasks requiring multifaceted reasoning and collaboration, from high-quality presentation generation to scientific research.

By Junwei Liao, Muning Wen, Jun Wang, Weinan Zhang

llms agents reinforcement-learning fine-tuning

arXiv AI

Jul 13

Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks

arXiv:2607. 09330v1 Announce Type: new Abstract: Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and service robotics.

By Nuocheng Yang, Sihua Wang, Zihan Chen, Tony Q. S. Quek, Changchuan Yin

llms agents reinforcement-learning robotics

arXiv Machine Learning

Jun 25

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

arXiv:2606. 24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems.

By Haggai Roitman

llms rag agents reinforcement-learning fine-tuning efficiency safety

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Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

Benchmarking Open-Ended Multi-Agent Coordination in Language Agents

Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

MARFT: Multi-Agent Reinforcement Fine-Tuning

Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems