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  "text": "AI-powered scientists are starting to take off!\n\nThis paper introduces PHYSMASTER, an LLM-based agent designed to operate as an autonomous theoretical and computational physicist.\n\nThe goal is to go from an AI co-scientist to an autonomous AI scientist in fundamental physics.\n\nPHYSMASTER uses Monte Carlo Tree Search for adaptive exploration, hierarchical agent collaboration for complex tasks, and LANDAU, a layered knowledge base preserving retrieved papers, curated prior knowledge, and validated methodology traces for reuse.\n\nFive case studies spanning from the cosmos to elementary particles demonstrate its capability:\n\nTwo acceleration cases: PHYSMASTER compressed labor-intensive engineering work that typically takes a senior PhD 1-3 months into less than 6 hours.\n\nTwo automation cases: Given human-specified hypotheses, PHYSMASTER executed end-to-end exploration loops in 1 day rather than unpredictable months.\n\nOne autonomous discovery case: PHYSMASTER independently explored an open problem in semi-leptonic decays of charmed mesons, constructing the effective Hamiltonian and predicting decay amplitudes using SU(3) flavor symmetry.\n\nPaper: https://t.co/gU6r38rOSM\n\nLearn to build effective AI agents in our academy: https://t.co/zQXQt0Pem8",
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