Alireza Shamsoshoara
AI / ML Engineer @ PyTorch-Meta
- Bay Area, California
- ResearchGate
- Github
- Google Scholar
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Daily AI Papers — August 19, 2026
12 minute read
Published:
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
12 minute read
Published:
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:
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
Daily AI Papers — August 16, 2026
10 minute read
Published:
1. Maglev: Sliding Recurrent Memory
Authors: Bo Liu, Qiang Liu arXiv: arxiv.org/abs/2608.02870 Summary: Maglev is a recurrent Transformer architecture with fixed-size memory that generalizes sliding-window attention while remaining parallelizable during training. It couples a prefiller that leverages full attention to produce memory targets with a decoder that uses only sliding-window attention and recurrent K/V injection to produce decoder memories for next-token prediction. Trending because: 9 HuggingFace upvotes + among the more-upvoted papers in this weekend’s feed.