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Published: January 01, 2199

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Daily AI Papers — August 19, 2026

12 minute read

Published: August 19, 2026

1. StateM: Reaching 95.3% Raw Accuracy, or a $15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling

Authors: Ziheng Qin, Yaxin Lu, Zhangyang Atlas Wang, Kai Wang arXiv: arxiv.org/abs/2608.15089 Summary: Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail to reactivate lessons from earlier executions, skip known procedures, or stop prematurely. Trending because: 284 HuggingFace upvotes + a timely benchmark drawing evaluation-focused attention

Daily AI Papers — August 18, 2026

13 minute read

Published: August 18, 2026

1. HarnessEval-W: Agentifying the Evaluation of Visual Worlds

Authors: Weiliang Chen, Haowen Sun, Jun Gao, Jiawei Chi, Hanyang Wang, Qiyu Dai, Yihao Li, Hao Li, Jingnan Gao, Yi-Hsin Hung, Xingzhuo Guo, Shangchen Miao, Zhiyuan Shi, Xiang Li, Fengrui Tian, Weihua Du, Ziqi Huang, Shenyuan Gao, Siqiao Huang, Mingyu Liu, Yifei Li, Shizun Wang, Xi Wang, Tianqi Zhang, Xue Luo, Xiyin Ren, Jinshan Ren, Xiaoyang Shen, Xiaobo Hu, Zhiyang Dou, Mingyu Ding, Yichao Yan, Xinchao Wang, Yizhou Wang, Shilong Liu, Wenzhao Zheng, Yueqi Duan, Yuan Gong, Ziwei Liu, Ming-Yu Liu, Jialong Wu, Jiangran Lyu, Fangfu Liu arXiv: arxiv.org/abs/2608.16859 Summary: A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, where judging a rollout requires understanding whether physics, causality, and world state evolve correctly. Trending because: 106 HuggingFace upvotes + tapping the surging interest in autonomous agents

Daily AI Papers — August 17, 2026

13 minute read

Published: August 17, 2026

1. Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination

Authors: Shuo Liang, Yixing Ma, Pengfei Zhou, Xingyan Chen, Zihan Mei, Manting Li, Feihan Chen, Zhiwen Wang, Bin Xu, Haotian Zhang, Jiajun Song, Shiya Su, Run Liu, Zhenghang Ni, Yifa Yu, Jintao Hong, Bolong Feng, Yifei Liu, Zirui Zhang, Jingxuan Zhang, Songlin Zhao, Yifan Bai, Kang Tan, Yizhe Liu, Junhao Du, Yongtao Ge, Zhaopan Xv, Xinyuan Zhang, Mengru Ma, Chunhua Shen, Wei Wang, Yang You, Zheng Zhu, Kaipeng Zhang, Wangbo Zhao arXiv: arxiv.org/abs/2608.14391 Summary: Recent video generators can fabricate realistic depictions of wars, disasters, public emergencies, and other real-world crises, creating substantial risks of misinformation. Existing benchmarks, however, provide limited evidence on detector and generator behavior in such settings, including how detectability varies with generation conditions, how people perceive generated videos, and whether detectors remain reliable during social dissemination. Trending because: 255 HuggingFace upvotes + a timely benchmark drawing evaluation-focused attention