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Locality-Sensitive Fingerprint for Stable AI Agent Skill Identity
AI agents acquire and execute skills at runtime, but current cryptographic hashing methods fail to maintain stable skill identities due to sensitivity to minor changes. The paper proposes a compact, locality-sensitive fingerprint to preserve similarity and enable better governance of agent skills.
Stable skill identity is crucial for managing and governing AI agents that dynamically fetch and execute skills. This approach improves tracking and verification of skills despite small modifications, enhancing reliability in AI agent ecosystems.
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Score 79
Source Type arxiv
Reposts 0
Topic Quality 65
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