Similarity Gates Approve Reversals: A Validity Audit of Embedding-Cosine Thresholds in Agent Systems
Why embedding-based similarity gates fail to detect dangerous instruction reversals in production AI agent workflows
Agent frameworks ship quality gates that compare text blocks by embedding-cosine similarity and decide at a fixed cutoff. Deduplication filters, semantic caches, drift guards, and answer grader gates deploy to answer the question: "Does this text still mean the same thing?" But t...
ArXiv CS.CL
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Current agent frameworks rely on flawed cosine similarity metrics that confuse superficial wording changes with fundamental meaning shifts. This research highlights a critical vulnerability where safety and drift-detection systems consistently fail because they measure text structure instead of semantic intent.
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