arXiv AI July 31, 2026 By Zhoupeng Shou, Xiaodong Hong, Congjing Ren, Jingdai Wang, Yongrong Yang, Zuwei Liao

A Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model Agents

arXiv:2607. 26594v1 Announce Type: cross Abstract: PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing responses, diagnosing deficiencies, adjusting gains, and validating the result.

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

arXiv AI

Jul 24

An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models

arXiv:2607. 21292v1 Announce Type: new Abstract: We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models.

By Ari Luna Rueda, Eike Cramer, Klaus Hellgardt, Mehmet Mercang\"oz

llms benchmarks

arXiv AI

Jul 14

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction

arXiv:2607. 09713v1 Announce Type: new Abstract: A key step toward autonomous industrial operation is the ability to create and reconfigure control policies from natural-language requirement specifications, with minimal or no manual redesign.

By Yuchen Wang, Javal Vyas, Tong Liu, Mehmet Mercangoz

llms agents efficiency safety

arXiv AI

Aug 7

Recursive Synthesis for Long-Horizon Terminal Tasks

arXiv:2608. 05466v1 Announce Type: new Abstract: High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent.

By Zhongzhi Li, Yucheng Shi, Zongxia Li, Ruhan Wang, Anhao Li, Zixun Huang, Junyao Yang, Lei Ke, Ninghao Liu, Haitao Mi, Leowei Liang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

1d ago

Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies

arXiv:2607. 22067v2 Announce Type: replace-cross Abstract: Competence claims for a language model in a safety-critical domain are credible when measured against a standard the domain already enforces.

By Isak Hwang, Yoon Pyo Lee, Syed Bahauddin Alam

llms rag fine-tuning multimodal safety

arXiv AI

Jul 29

Reinforcement Learning for Code Optimization

arXiv:2607. 25970v1 Announce Type: cross Abstract: RL for code correctness is now established: have the model generate a program, run it against hidden test cases, and reward solutions that pass.

By Pierre Chambon, Kunhao Zheng, Juliette Decugis, Benoit Sagot, Gabriel Synnaeve

reinforcement-learning efficiency

arXiv AI

Jun 18

Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier

arXiv:2606. 18284v1 Announce Type: cross Abstract: The limiting resource for training agents via reinforcement learning (RL) is increasingly frontier task supply: valid, solvable tasks just difficult enough to train the current model.

By Lorenz Wolf, Connor Watts, Roger Creus Castanyer, Geoffrey Bradway, Maxwill Lin, Augustine N. Mavor-Parker, Matthew Daborn-Sargent

agents reinforcement-learning

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An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction

Recursive Synthesis for Long-Horizon Terminal Tasks

Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies

Reinforcement Learning for Code Optimization

Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier