Author

A. Ghosh

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Preprint Aug 2026

CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discovery

Many scientific discovery problems require searching combinatorial hypothesis spaces under complex domain constraints. Reinforcement learning (RL) offers a promising approach, but existing methods rely on scalar rewards that provide limited information about why candidate solutions fail, leading agents to repeatedly ex...

Piyush Jha, Jake Rudolph, V. Knapp-Pérez et al. · 3 citations

#artificial intelligence Preprint Sep 2026

Searching for BSM Experimental Signatures with Large Lagrangian Models

The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminating experimental observations. A case in point is dark matter, where the overwhelming gravitational evidence only goes so far in distinguishing between models within a vast...

Ibrahim Elsharkawy, V. Knapp-Pérez, W. Bhimji et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Towards Certificate-Driven Software Porting: A Self-Improving Agentic Harness for Scientific Program Optimization

CDES is introduced, which extends evolutionary search with enforceable restrictions derived from failed candidates, recorded as certificates of assumptions, checker evidence, and justified restrictions, to CPU-to-GPU translation of two particle-simulation functions from the Geant4 toolkit.

Piyush Jha, A. Ghosh, Vijay Ganesh · 0 citations