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SAGA reduces the incidence of empty-result queries by systematically grounding SPARQL generation in schema constraints, outperforming existing methods in accuracy across multiple benchmarks.
NIMO bridges the gap between diverse AI algorithms and robotic systems, enabling rapid materials discovery without requiring programming expertise.
Materials synthesis just got a reasoning upgrade: a new benchmark reveals the limitations of current methods, while a novel provenance-grounded framework leaps ahead in out-of-distribution performance.
LLMs can autonomously navigate the notoriously complex task of alloy phase diagram construction, outperforming traditional ML methods and even exhibiting complementary strengths when combined with domain-specific models.
