SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
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A bilevel optimization framework smooths isotonic regression outputs into continuous piece-wise linear monotonic functions to recover marginal properties in both convex and non-convex cases.
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Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science
SEED is a structural encoding framework using typed actor-flow graphs to describe, evaluate novelty of, and generate experimental designs for AI-enabled science under feasibility and governance constraints.
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Piece-wise linear isotonic regression
A bilevel optimization framework smooths isotonic regression outputs into continuous piece-wise linear monotonic functions to recover marginal properties in both convex and non-convex cases.