Author

Nan Huang

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#artificial intelligence Review Sep 2026

DISCERN: Can AI Agents Work Like Scientists and Guide Discovery?

Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially reducing routine scientific workload while allowing scientists to focus on interpretation and discovery. Existing benchmarks often only assess analytical task completion or hyp...

Nan Huang, Mario Tapia-Pacheco, Kun Zhou et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Can LLMs Discover Scientific Laws in Real and Parallel Worlds?

It is found that predictive fit can diverge from scientific validity, memorization shapes whether models reproduce or move beyond published formulas, and the best-of-N study reveals a selection bottleneck.

Yi-Ming Huang, Zi-Chen Liu, Junxia Cui et al. · 2 citations