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Author

Shuyang Zhang

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#artificial intelligence Review Sep 2026

What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents

Background. A component replacement in a language-model agent changes an execution trajectory, potentially altering later observations, resource use, and recovery opportunities. Different evidence is needed to assess its task-level benefit and the contribution of local decision quality. Methods. This critical scoping r...

Shu-Yang Zhang, Jian-Shuo Chang · 0 citations
#artificial intelligence Review Sep 2026

What Does a Skill Actually Do? Estimands and Evaluation Validity for Tool and Skill Use in LLM Agents: A Critical Review

This critical narrative review examines what these evaluations estimate and which conclusions their designs support and provides a methodological synthesis and a reporting checklist to help align claims about tools and skills with the comparisons their evaluation designs support.

Shu-Yang Zhang · 1 citation · ⚡1
Preprint Aug 2026

Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverable

Scaling model-generated data is usually viewed as improving distillation: more examples should increase coverage, reduce noise, and produce stronger students. We show a second effect: larger datasets can make subtle teacher-specific signals easier to detect in the trained student, even when examples are off-task and ne...

Zhichen Dong, Zhi-Xuan Liu, Yuanjiu Fan, et al. · 1 citation
Conference Open access 2026

Adaptive Prompt Optimization for Open-Ended Tasks: Uncertainty Preference as a Secondary Signal

A semantic-entropy-based method, using task uncertainty to guide prompt optimization, which requires no training, works with black-box models, and integrates easily into existing prompt optimizers.

Shuyang Zhang, Zhixuan Liu, Zhichen Dong et al. · 2 citations

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