Coding agents can operate complex scientific software efficiently by learning just the simulator's interface contract (vocabulary, constraints, validation rules) rather than the full domain—and these adapters can improve themselves by learning from past attempts.
SIGA helps general-purpose coding agents learn to use specialized scientific simulators by providing a lightweight adapter that teaches them the simulator's input language, validation rules, and constraints.