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A workshop paper presenting a single-instance case study in which an AI agent is asked to rediscover a hidden invariant of degree-four Blaschke curves from numerical data, yielding a homogeneous cubic that predicts unseen configurations with machine-precision residuals, while honestly noting limitations such as a deterministic polynomial-fitting baseline matching the agent's result.
This paper investigates growth dynamics in deterministic equational discovery across three toy substrates and two real-world replications, finding substrate-conditional saturating power-law scaling.