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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.
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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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Addressing the Synergy Gap: The Six Elements of the Design Space
The paper proposes six interconnected elements of a design space to close the synergy gap in human-AI decision-making.