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Language-model agents in SwarmWorld can self-organize to build resilient technological societies, surpassing isolated search methods in innovation and adaptability.
Mechanism recovery in AI-generated hypotheses falters in early reasoning stages, revealing vulnerabilities in scientific validity that could mislead experimental planning.
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.
