SEVerA uses Formally Guarded Generative Models and a three-stage Search-Verification-Learning process to synthesize self-evolving agents that satisfy hard formal constraints while improving task performance.
Neural-guided symbolic regression with asymptotic constraints
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PySR delivers a distributed evolutionary symbolic regression tool with a new EmpiricalBench for recovering historical scientific equations from data.
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SEVerA: Verified Synthesis of Self-Evolving Agents
SEVerA uses Formally Guarded Generative Models and a three-stage Search-Verification-Learning process to synthesize self-evolving agents that satisfy hard formal constraints while improving task performance.
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Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
PySR delivers a distributed evolutionary symbolic regression tool with a new EmpiricalBench for recovering historical scientific equations from data.