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Hanwen Xing

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ContractBench: Can LLM Agents Preserve Observation Contracts?

This work shows that observation contract compliance is an emergent, regression-prone capability: it is neither guaranteed by general tool-use ability nor consistently improved by larger or newer models.

Ji-Cheng Wang, Yifeng He, Zi-Li Wang et al. · 1 citation

A Very Big Video Reasoning Suite

The Very Big Video Reasoning (VBVR) Dataset is introduced, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets.

Maijunxian Wang, Rui-Si Wang, Ju-Yi Lin et al. · 28 citations · ⚡5
Book Open access Aug 2026

Recipes for Agents: Understanding Skills and Their Open Questions

This paper examines how skills may help address bottlenecks of current agents and how they may expand agent capabilities through reusable domain procedures loaded at inference time and outlines open questions in skill construction, composition, evaluation, portability, governance, and security.

Hanwen Xing, Haomin Zhuang, Xuandong Zhao et al. · 7 citations · ⚡1
Book Open access Aug 2026

Recipes for Agents: Understanding Skills and Their Open Questions

As Large Language Model (LLM) agents have demonstrated broad competence, but they still struggle in specialized, real-world workflows. Existing approaches such as RAG, fine-tuning and tool integration improve knowledge access, model adaptation, and external functionality, yet they do not fully address a central gap: th...

Hanwen Xing, Haomin Zhuang, Xuandong Zhao et al. · 6 citations · ⚡1
Review Aug 2026

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.

Xiaomin Li, Yuexing Hao, Jian Hou et al. · 1 citation
Preprint Jul 2026

CurveShift: Is Agent Progress Scalar? Separating Level from Shape

Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summaries capture the overall performance, but they do not test whether progress is distributed differently across task difficulty. We find that...

Hanwen Xing, Pengyu Wang, Bingxu Meng et al. · 1 citation
Preprint Aug 2026

Demystifying Agent Skills: Why They Work-Until They Don't

This work designs a contrastive study that combines controlled quantitative experiments with paired trajectory analysis and consolidates observations into a taxonomy of three high-level categories and twelve skill-use modes, showing that skills work when noisy trajectories become procedural anchors that stabilize execu...

Zhi-Yuan Jiang, Fan Huang, Hanwen Xing et al. · 4 citations · ⚡1

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