Koji Tsuda

The paper introduces ProvMind, a provenance-grounded reasoning framework that significantly improves materials synthesis process optimization by accurately predicting optimal synthesis routes under challenging, out-of-distribution conditions.

The paper proposes SAGA, a framework for schema-aware grounding in agentic text-to-SPARQL generation, which maintains a persistent type state, filters incompatible property candidates, and handles missing schema information permissively.