arXiv AI

World-Model Collapse as a Phase Transition

arXiv:2606. 31399v1 Announce Type: new Abstract: Water looks unchanged as it warms, then at a critical point it boils.

arXiv AI

Jul 21

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

arXiv:2607. 16204v1 Announce Type: new Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments.

By Darshan Deshpande

llms agents diffusion reinforcement-learning fine-tuning efficiency safety

arXiv AI

Jun 24

World Models in Pieces: Structural Certification for General Agents

arXiv:2606. 24842v1 Announce Type: new Abstract: In the big-world regime, agents cannot be universally capable and their ability is inevitably specialized across a world model in pieces.

By Yikai Lu, Yifei Wu, Xinyu Lu, Tongxin Li

agents

arXiv AI

Jul 7

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents

arXiv:2606. 27806v3 Announce Type: replace Abstract: Language agents plan by generating not only actions but also implicit predictions of how the world will change.

By Xinyuan Song, Zekun Cai

llms agents benchmarks

arXiv AI

Jun 29

Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents

arXiv:2606. 27806v1 Announce Type: new Abstract: World models for language agents come in two useful forms.

By Xinyuan Song, Zekun Cai

llms agents benchmarks safety

arXiv AI

Jun 2

COMAP: Co-Evolving World Models and Agent Policies for LLM Agents

arXiv:2606. 02372v1 Announce Type: new Abstract: Equipping language agents with world models enables them to anticipate environment dynamics and evaluate candidate actions before execution.

By Youwei Liu, Jian Wang, Hanlin Wang, Wenjie Li

llms agents robotics efficiency benchmarks

arXiv AI

Jun 9

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

arXiv:2604. 22748v2 Announce Type: replace Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck.

By Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin, Lingdong Kong, Jize Zhang, Teng Tu, Weijian Ma, Ziqi Huang, Senqiao Yang, Wei Huang, Yeying Jin, Zhefan Rao, Jinhui Ye, Xinyu Lin, Xichen Zhang, Qisheng Hu, Shuai Yang, Leyang Shen, Wei Chow, Yifei Dong, Fengyi Wu, Quanyu Long, Bin Xia, Shaozuo Yu, Mingkang Zhu, Wenhu Zhang, Jiehui Huang, Haokun Gui, Runyi Li, Shiyi Du, Xu Huang, Dong Huang, Rui Liu, Chenyu Tang, Xuhang Chen, Chengzu Li, Haoxuan Che, Long Chen, Qifeng Chen, Wenxuan Zhang, Wenya Wang, Xiaojuan Qi, Yang Deng, Yanwei Li, Mike Zheng Shou, Zhi-Qi Cheng, See-Kiong Ng, Ziwei Liu, Philip Torr, Jiaya Jia

agents diffusion reinforcement-learning

arXiv AI

Jul 13

Prompt-Driven Exploration

arXiv:2607. 08837v1 Announce Type: cross Abstract: Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers.

By Sunshine Jiang, John Marangola, David Zhang, Raghuram Kowdeed, Ruiyang Luo, Nitish Dashora, Richard Li, Pulkit Agrawal, Zhang-Wei Hong

llms reinforcement-learning robotics multimodal

arXiv AI

Jun 12

Topical Phase Transitions in Artificial Intelligence Research: Large-Scale Evidence and an Early-Warning Signature for Emerging Topics

arXiv:2606. 12828v1 Announce Type: new Abstract: Do research topics in artificial intelligence grow gradually, or do they advance through abrupt, detectable jumps?

By Rasul Khanbayov, Hasan Kurban

llms rag agents diffusion computer-vision reinforcement-learning multimodal

arXiv AI

Jul 8

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade

arXiv:2607. 06503v1 Announce Type: new Abstract: Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantial inference compute before the failure becomes observable.

By Kai Ruan, Zihe Huang, Ziqi Zhou, Qianshan Wei, Xuan Wang, Hao Sun

llms agents

arXiv Machine Learning

Jun 18

Stealthy World Model Manipulation via Data Poisoning

arXiv:2606. 18697v1 Announce Type: new Abstract: Model-based learning agents use learned world models to predict future states, plan actions, and adapt to new environments.

By Yibin Hu, Xiaolin Sun, Zizhan Zheng

agents robotics fine-tuning safety

arXiv AI

Jul 1

Ask the World Before Acting: Budgeted Environment Probing for World-Model Calibration

arXiv:2606. 31422v1 Announce Type: new Abstract: Long-horizon language agents do not only choose actions; they carry a private model of the world from one decision to the next.

By Xinyuan Song, Zekun Cai

agents

Hugging Face Trending Papers

Aug 3

Quo Vadis, World Modeling?

Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize. World modeling offers a natural intermediate proxy that allows agents to query lower-cost, more controllable feedback before committing to real actions.

agents

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