COCOCO is a conformal framework for NeSy-CBMs that jointly conformalizes concepts and labels, reconciles them via deduction-abduction revision, and satisfies consistency, coverage, and conciseness while retaining distribution-free guarantees.
Mell et al
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Optimizing code for semantic density rather than human readability can improve agentic AI development efficiency, but aggressive compression of logs increased overall costs by shifting burden to reasoning.
citing papers explorer
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Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
COCOCO is a conformal framework for NeSy-CBMs that jointly conformalizes concepts and labels, reconciles them via deduction-abduction revision, and satisfies consistency, coverage, and conciseness while retaining distribution-free guarantees.
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Beyond Human-Readable: Rethinking Software Engineering Conventions for the Agentic Development Era
Optimizing code for semantic density rather than human readability can improve agentic AI development efficiency, but aggressive compression of logs increased overall costs by shifting burden to reasoning.