FragileFlow formalizes margin-aware error flow and applies spectral control through a calibrated margin buffer and class-wise risk matrix, supported by a PAC-Bayes bound, to enhance worst-class robustness in foundation model adaptation while preserving clean accuracy.
Tapas are free! training-free adaptation of programmatic agents via llm-guided program synthesis in dynamic environments
2 Pith papers cite this work. Polarity classification is still indexing.
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Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
citing papers explorer
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FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness
FragileFlow formalizes margin-aware error flow and applies spectral control through a calibrated margin buffer and class-wise risk matrix, supported by a PAC-Bayes bound, to enhance worst-class robustness in foundation model adaptation while preserving clean accuracy.
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Responsible Agentic AI Requires Explicit Provenance
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.