The paper introduces DoublesEval, a diagnostic framework that uses professional doubles badminton to test visual‑language models’ ability to reason about dynamic multi‑agent interactions. It decomposes rallies into key moments and evaluates models across four dimensions—atomic recognition, intra‑segment composite understanding, cross‑segment causal reasoning, and high‑level tactical abstraction—highlighting specific reasoning failures. The authors also propose TacticCheck, a lightweight consistency checker that improves performance without retraining the models, yet significant gaps remain in tactical reasoning.
By Jintao Cheng, Weibin Li
Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated roles and spatial-te...
arXiv:2609.25001v1 Announce Type: new
Abstract: Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, a...
By Yiran Wang, Xingyilang Yin, Junfu Pu, Guangzhi Wang, Kaifeng Li, Mingyu Ouyang, Huiqiang Sun, Lingen Li, Cheng Cheng, Wangbo Yu, Honghao Chen, Xiaodong Cun, Chi-Man Pun, Zhiguo Cao, Ying Shan
arXiv:2609.38881v1 Announce Type: new
Abstract: Real-time strategy (RTS) games require agents to coordinate economic development, production and construction, base defense, unit organization, and att...
By Xinhe Tian, Xiaoyue Zhang, Ziyou Zhang, Jiacheng Li, Xiaoqiang Jin, Qianchuan Zhao, Gaochen Cui
arXiv:2606. 09826v1 Announce Type: cross Abstract: Vision-language model (VLM) agents are increasingly deployed in interactive game environments.
By Mingxian Lin, Shengju Qian, Yuqi Liu, Yi-Hua Huang, Yiyu Wang, Wei Huang, Yitang Li, Fan Zhang, Zeyu Hu, Lingting Zhu, Xin Wang, Xiaojuan Qi
The paper introduces LM Fight Arena, a new benchmark that pits large multimodal models against each other in the fighting game Mortal Kombat II to evaluate real‑time visual understanding and sequential decision‑making. Six leading open‑ and closed‑source models were tested in a controlled tournament where each controlled the same character, ensuring a fair comparison. The framework offers a fully automated, reproducible, and objective assessment of an LMM’s strategic reasoning in a dynamic setting.
By Yushuo Zheng, Tongrui Ye, Zicheng Zhang, Xiongkuo Min, Huiyu Duan, Guangtao Zhai
arXiv:2606. 29932v3 Announce Type: replace Abstract: Long-horizon strategic planning in complex strategy games requires coordinating tightly coupled decision domains, including technology, economy, diplomacy, and military, across hundreds of turns under imperfect information.
By Tianyu Jin, Shuo Chen, Yida Wang, Liuyu Xiang, Yingzhuo Liu, Zhiyao Jiang, Yexin Li, Peipei Li, Zhaofeng He
arXiv:2606. 14397v1 Announce Type: new Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities.
By Mykola Vysotskyi, Runqi Lin, Grzegorz Biziel, Michal Zakrzewski, Sebastian Montagna, Damian Rynczak, Shreyansh Padarha, Kumail Alhamoud, Zihao Fu, William Lugoloobi, Kai Rawal, Hanna Yershova, Xander Davies, Taras Rumezhak, Guohao Li, Fazl Barez, Baoyuan Wu, Arkadiusz Drohomirecki, Yarin Gal, Chris Russell, Christopher Summerfield, Adam Mahdi, Volodymyr Karpiv, Philip Torr, Adel Bibi
arXiv:2608. 12626v1 Announce Type: cross Abstract: Strategic reasoning in Large Language Models (LLMs) within long-horizon environments is often limited by inconsistent subgoals.
By Yi Wu, Zhimin Hu
UnifiedPlayers is a cooperative framework that jointly adapts planning, execution, and evaluation for tool-integrated reinforcement learning agents. It consists of a Planning Player that generates tasks, an Execution Player that creates multi-turn trajectories with Python tool calls, and an Evaluation Player that builds executable verifiers, all coordinated by role‑specific rewards under GRPO. The approach outperforms prior baselines on mathematical and general reasoning benchmarks and yields a verifier with high adversarial detection accuracy and more discriminative reward signals.
By Wenjie Liao, Liangjie Zhao, Zehong Cao
arXiv:2606.14397v4 Announce Type: replace
Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capab...
By Mykola Vysotskyi, Runqi Lin, Grzegorz Biziel, Michal Zakrzewski, Sebastian Montagna, Damian Rynczak, Shreyansh Padarha, Kumail Alhamoud, Zihao Fu, William Lugoloobi, Kai Rawal, Hanna Yershova, Taras Rumezhak, Guohao Li, Fazl Barez, Baoyuan Wu, Arkadiusz Drohomirecki, Yarin Gal, Chris Russell, Christopher Summerfield, Adam Mahdi, Volodymyr Karpiv, Philip Torr, Adel Bibi
arXiv:2609.21562v1 Announce Type: cross
Abstract: Coding agents can modify and test code across large software projects. Game development is a domain where agents must implement gameplay rules. A gam...
By Xinyu Che, Yunfei Ge, Shihao Li, Yanchen Liu, Hang Yan, Xinping Lei, Yanghai Wang, Zixuan Dong, Yifan Yao, Qianqian Xie, Letian Zhu, Jiaheng Liu