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From the 2 of 26 linked papers with an AI index.

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20242026
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cs.AI2026

OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models

Qiushi Sun, Kanzhi Cheng, Yian Wang +20

The paper introduces OSReward, a benchmark for evaluating vision-language model judges that assess computer-using agent trajectories, and presents open reward models (OS‑Shepherd)…

cs.AI2026

Beyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training

Jiawen Tao, Miao Peng, Yaoming Li +7

The paper introduces a pipeline that creates synthetic textbooks by clustering source material, planning hierarchical tables of contents, and assembling sections into full books, s…

cs.AI2026

Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration

Qifan Zhang, Dongyang Ma, Tianqing Fang +5

Most agents today ``self-evolve'' by following rewards and rules defined by humans. However, this process remains fundamentally dependent on external supervision; without human gui…

cs.AI2026

OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis

Kanzhi Cheng, Zehao Li, Zheng Ma +11

Mobile agents powered by vision-language models have demonstrated impressive capabilities in automating mobile tasks, with recent leading models achieving a marked performance leap…

cs.AI2026

Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problems

Qifan Zhang, Jianhao Ruan, Aochuan Chen +4

Large Reasoning Models (LRMs) have advanced rapidly; however, existing benchmarks in mathematics, code, and common-sense reasoning remain limited. They lack long-context evaluation…

cs.AI2026

AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?

Henan Sun, Kaichi Yu, Yuyao Wang +5

Reasoning ability has become a central focus in the advancement of Large Reasoning Models (LRMs). Although notable progress has been achieved on several reasoning benchmarks such a…