arXiv AI

Evaluating LLMs as Interpretable Controllers for Dynamical Systems

arXiv:2607. 22609v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems remains largely unexplored.

arXiv AI

Aug 5

Uncovering Spontaneous Physics Representations in In-Context Learning

arXiv:2508. 12448v2 Announce Type: replace-cross Abstract: In-context learning (ICL) lets large language models (LLMs) solve new tasks from prompts alone, across an ever-widening range of domains, yet the mechanisms underlying this ability remain poorly understood.

By Yeongwoo Song, Jaeyong Bae, Dong-Kyum Kim, Hawoong Jeong

llms

arXiv AI

Jun 8

Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control

arXiv:2512. 23292v5 Announce Type: replace Abstract: The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface.

By Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi, Sajedul Talukder, Diab Abueidda, Seid Koric, Souvik Chakraborty, Syed Bahauddin Alam

llms agents reinforcement-learning multimodal safety

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 Machine Learning

Jul 31

Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control

arXiv:2607. 27914v1 Announce Type: new Abstract: Multi-zone variable-air-volume control must balance thermal comfort, indoor air quality, and electricity use across several continuous actuators.

By Takumi Shioda, Kohei Terashima, Tatsuo Nagai

llms reinforcement-learning fine-tuning

Hugging Face Trending Papers

Jun 25

In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and physical realism. Large language model (LLM)-based approaches can interpret diverse open-vocabulary instructions and compose high-level action plans, but they often generate motions that violate physical constraints.

llms benchmarks safety

arXiv Machine Learning

Jul 15

Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control

arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.

By Takumi Shioda, Kohei Terashima, Tatsuo Nagai

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 26

In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

arXiv:2606. 26981v1 Announce Type: cross Abstract: Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and physical realism.

By Xiaomeng Fu, Junfan Lin, Yang Liu, Yaowei Wang, Guanbin Li, Liang Lin, Ziliang Chen

llms benchmarks safety

arXiv Machine Learning

Aug 7

STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts

arXiv:2602. 14265v3 Announce Type: replace-cross Abstract: Inference-Time-Compute (ITC) methods like Best-of-$n$ and Tree-of-Thoughts are meant to produce output candidates that are both high-quality and diverse, but their use of high-temperature sampling often fails to achieve meaningful output diversity.

By Zachary Bamberger, Till R. Saenger, Gilad Morad, Ofra Amir, Brandon M. Stewart, Amir Feder

llms safety

arXiv Machine Learning

Jun 9

GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing

arXiv:2606. 08238v1 Announce Type: new Abstract: Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material microstructure in manufacturing processes.

By Soumik Dutta, Kiarash Naghavi Khanghah, Sania Shree, Logan McNeil, Thomas Feldhausen, Hongyi Xu, Rajiv Malhotra

llms agents benchmarks safety

arXiv Machine Learning

Jul 7

Reduced-Order Models: The Mother of World Models

arXiv:2607. 03198v1 Announce Type: new Abstract: World models -- compressed latent representations of an environment that support action-conditioned prediction and planning -- are typically presented as a product of modern self-supervised learning.

By Rajat Ghosh

computer-vision

arXiv Machine Learning

Jun 8

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

arXiv:2511. 02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capacity to imagine and choose -- to simulate future scenarios, weigh trade-offs, and act with calibrated uncertainty.

By Farhad Rezazadeh, Amir Ashtari Gargari, Hatim Chergui, Sandra Lagen, Merouane Debbah, Houbing Song, Lingjia Liu

llms agents

arXiv Machine Learning

Jun 2

Can Vision Language Models Learn Intuitive Physics from Interaction?

arXiv:2602. 06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world.

By Luca M. Schulze Buschoff, Konstantinos Voudouris, Can Demircan, Eric Schulz

llms reinforcement-learning fine-tuning

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