arXiv AI August 13, 2026 By Jasin Cekinmez, Addison J. Wu, Haotian Xia, Kyumin Andrew Shim, Anay Putty, Jinglin Xiao, Zhuohan Liu, Leo Liu, Weining Shen

SportD: How do VLMs physically strategize?

Read the original on arXiv AI →

arXiv:2607. 14616v3 Announce Type: replace Abstract: Vision-language models (VLMs) can describe a scene, but can they act well within one?

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

arXiv AI

Jul 17

SportD: Can VLMs Physically Strategize?

arXiv:2607. 14616v1 Announce Type: new Abstract: Vision--language models have become increasingly capable of interpreting visual scenes, but it remains unclear whether they can use information to make strategically effective decisions.

By Jasin Cekinmez, Addison J. Wu, Haotian Xia, Akshaya Bharadhwaj, Anay Putty, Anirudh Ravishankar, Jaewoong Lee, Jinglin Xiao, Kyumin Andrew Shim, Mishika Ahuja, Nisarga Patil, Leo Liu, Zhuohan Liu, Weining Shen

llms benchmarks

arXiv Machine Learning

Jun 16

How Should World Models Be Evaluated? A Decision-Making-Centric Position

arXiv:2606. 15032v1 Announce Type: new Abstract: World models have rapidly become one of the central abstractions in modern AI.

By Yang Yu, Shiyuan Zhang, Yifei Sheng, Haoxiang Ren, Haoxin Lin

robotics benchmarks

arXiv Machine Learning

Aug 4

Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races

arXiv:2608. 01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma.

By Phu Hoa Pham, Duy Minh Dao Sy, Trung Kiet Huynh, Phu Quy Nguyen Lam, Chi Nguyen Tran, Minh Trung Le, Phong Hao Le, Dinh Nam Nguyen, Thien Ky Nguyen Dong, Elias Fernandez Domingos, Le Hong Trang, The Anh Han

llms agents benchmarks safety

arXiv AI

Jul 21

FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches

arXiv:2607. 17765v1 Announce Type: cross Abstract: We introduce WC2026-Agents, a benchmark and dataset for evaluating large language models (LLMs) as autonomous forecasting agents on real, future events.

By Jiacheng Ding, Cong Guo, Jason Xu

llms agents benchmarks

arXiv AI

Jul 1

Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning

arXiv:2606. 30686v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) systems, built on pretrained vision-language models (VLMs), have shown rapidly improving performance on robot manipulation benchmarks.

By Taozhao Chen, Ian Manchester, Huaming Chen

llms robotics multimodal benchmarks

arXiv Machine Learning

Jun 30

How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position

arXiv:2606. 15032v2 Announce Type: replace Abstract: World models have become a central abstraction in modern AI.

By Yang Yu, Shiyuan Zhang, Yifei Sheng, Haoxiang Ren, Haoxin Lin

robotics

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