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The model reveals that physical relationships in materials science are more accessible through controlled transformations than through static representations, challenging assumptions about how LLMs encode scientific knowledge.
Graph-native reinforcement learning can boost hypothesis generation in materials science by achieving up to 65% better traceability than traditional models.
