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cs.AI2026

ReproAgent: Contract-Guided Paper-to-Code Reproduction

Xue Hu, Zewei Pan, Zhongyuan Wang +3

Paper-to-code reproduction asks scientific AI agents to turn research papers into executable repositories that preserve the paper's method, protocol and artifacts. This is difficul…

cs.AI2026

SA-Bench: Evaluating Semantic Alignment in LLM-Based Paper Reproduction

Xue Hu, Zewei Pan, Zeli Su +2

LLM agents can generate paper reproduction code, yet often produce scientifically unfaithful implementations. We define this failure mode as semantic drift, where generated code si…

cs.AI2026

ExRole: From Team Trajectories to Executable Roles in Multi-Agent Language Models

Zhou Liu, Chaoyang Han, Zewei Pan +2

Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behav…

cs.AI2026

SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation

Zhou Liu, Ligang Huang, Zeli Su +5

Computer-using agents can perceive rich software interfaces, yet their decisions often lack visual procedural memory: they may recognize individual controls without identifying whi…

cs.AI2026

The Curse of Helpfulness: Inverse Scaling Law in Robustness to Distractor Instructions via DistractionIF

Zeli Su, Zhankai Xu, Tianlei Chen +4

Large Language Models (LLMs) are increasingly deployed in agentic and retrieval-augmented generation (RAG) systems, where they must execute user-specified tasks over externally pro…