arXiv AI June 10, 2026 By Jiaxuan Chen, Haonan Li, Yang Shu

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606. 11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop interaction, and local numerical optimization from natural initializations stalls in the resulting non-convex cost landscape.

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

arXiv AI

Jun 29

When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model

arXiv:2606. 21641v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been proposed as hyperparameter-optimization (HPO) advisors that "warm-start" search from prior knowledge, proposing strong configurations in very few evaluations.

By Carson Rodrigues, Oysturn Vas, Isaiah Abner DCosta, Nithish Kumar Prabhakaran

llms benchmarks

arXiv Machine Learning

22h ago

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

arXiv:2608. 17310v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning.

By Zhi Zheng, Rongsheng Chen, Yunpeng Ba, Zhenkun Wang, Yee Whye Teh, Wee Sun Lee

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 17

MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization

arXiv:2606. 17526v1 Announce Type: new Abstract: Efficient optimization is essential for training large language models.

By Da Chang, Ganzhao Yuan

llms fine-tuning safety

arXiv AI

Jul 21

MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

arXiv:2607. 18006v1 Announce Type: cross Abstract: Large language models achieve strong reasoning performance, but often at prohibitive training cost - a challenge that is especially acute for compact models ($\leq 4 \, \mathrm{B}$ parameters) trained under limited budgets.

By Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Zifeng Ding, Volker Tresp, Yunpu Ma

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Aug 11

A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization

arXiv:2608. 08156v1 Announce Type: new Abstract: In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously updates structural components (like control flow) and continuous parameters.

By V\'ictor Gallego

llms

arXiv Machine Learning

Jul 8

Regularity and Stability Properties of Selective SSMs with Discontinuous Gating

arXiv:2505. 11602v3 Announce Type: replace Abstract: Selective State-Space Models (SSMs) such as Mamba have become central to long-sequence modeling.

By Nikola Zubi\'c, Davide Scaramuzza

Related stories

When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization

MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization

Regularity and Stability Properties of Selective SSMs with Discontinuous Gating