Author

Daniel Khashabi

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#artificial intelligence May 2026

Can Coding Agents Reproduce Findings in Computational Materials Science?

This work presents AutoMat, a benchmark for evaluating LLM-based agents'ability to reproduce claims from computational materials science, and curates a set of claims from real materials science papers to test whether coding agents can recover and execute the end-to-end workflow needed to support (or undermine) such cla...

Ziyang Huang, Yiyun Cao, A. Shargh et al. · arXiv.org · 9 citations

#machine learning Preprint Aug 2026

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

This work introduces VBVR-Pro, a closed-loop testbed that makes native visual reasoning through generation trainable, verifiable, optimizable, and experimentally controllable, and identifies recurring failure modes of the prevalent VLM-as-a-judge paradigm.

Junhua Xu, Rui-Si Wang, Fanyi Pu et al. · 5 citations

Preprint Aug 2026

Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge

The Information Abundance Paradox is supported and it is suggested that scaling toward near-infinite context is not simply a matter of supplying more data, even when high-quality long-context data is abundant.

Arda Uzunouglu, Benjamin Van Durme, Daniel Khashabi · 2 citations