A finite sheaf-theoretic framework ranks obstruction measures to identify when an AI agent's theory must deform within its language or extend to a new one, validated on a controlled transition benchmark.
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Input/output constraints boost LLM-generated decision model structural similarity to gold standards by 37-54%, with models matching gold outcomes on 51-53% of test scenarios while removing redundant logic.
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Sheaf-Theoretic Transport and Obstruction for Detecting Scientific Theory Shift in AI Agents
A finite sheaf-theoretic framework ranks obstruction measures to identify when an AI agent's theory must deform within its language or extend to a new one, validated on a controlled transition benchmark.
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From Legal Text to Executable Decision Models: Evaluating Structured Representations for Legal Decision Model Generation
Input/output constraints boost LLM-generated decision model structural similarity to gold standards by 37-54%, with models matching gold outcomes on 51-53% of test scenarios while removing redundant logic.