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Executable Code Knowledge enables coding agents to carry validation evidence directly within code, achieving perfect precision and recall in patch tasks where traditional methods fail.
Event presence can mislead interpretations of skill execution fidelity, revealing critical discrepancies in agent performance assessments.
Repeated revisions in coding agents can lead to a significant drop in reliability, highlighting the need for structured evidence-bound contracts in code repair processes.
Task decomposition quality is the critical bottleneck in skill composition, and our Iterative Skill-Aware Decomposition method dramatically boosts accuracy and retrieval performance.
Moderate geometric convergence in LLMs can coexist with near-perfect functional transfer, challenging assumptions about structural alignment across architectures.