Author

Ke-Xun Zhang

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Review Open access Sep 2026

Unifying AI-assisted scientific discovery around exploration, hypothesis generation, and testing

Summary Large language models (LLMs) and agent systems increasingly support scientific discovery across domains. Yet the literature remains fragmented around isolated tasks, with limited attention to how components form integrated workflows. Here, we introduce exploration, hypothesis generation, and testing (EXHYTE) as...

Musaddaqul Hasib, Sumin Jo, Harsh Sinha et al. · Patterns · 0 citations

#artificial intelligence Preprint Oct 2026

Scalable Weak-to-Strong Verifier Hardening for Agent Environments

Agent environments are essential for large language model (LLM) development. They use programmatic verifiers to turn complex outcomes into rewards. Verifiers can be attacked into awarding full credit to cheating agents without completing the intended task, causing inaccurate evaluation and misalignment in training. As...

Chong-Fang Zeng, Zi-Qian Zhong, Ke-Xun Zhang et al. · 2 citations