Author

Abhinaba Basu

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#artificial intelligence Mar 2026

Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials

Machine-learned interatomic potentials (MLIPs) are deployed for high-throughput materials screening without formal reliability guarantees. We show that a single MLIP used as a stability filter misses 93% of density functional theory (DFT)-stable materials (recall 0.07) on a 25,000-material benchmark. Proof-Carrying Mat...

Abhinaba Basu, Pavan Chakraborty · arXiv.org · 1 citation

#artificial intelligence Mar 2026

ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs

ICE (Intervention-Consistent Explanation), a framework that evaluates explanations against random baselines of equal size under multiple operators, is introduced, showing that faithfulness is not a fixed property but an operator-dependent quantity that changes with the intervention method used to measure it.

Abhinaba Basu, Pavan Chakraborty · arXiv.org · 2 citations