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This paper presents a theoretical cost accounting for exhaustive sweeps and sequential mutations on reactive computational graphs, deriving speedup ratios and validating them on a Julia-based graph engine.
The author compares Structural Equation Modeling, Neural ODEs, and AI Programs like DSPy as declarative frameworks for defining and optimizing computational graphs, arguing that structured flows are essential for trustworthy AI agents.