Both criticisms are fair. The RMS corpus example is exactly the false positive failure mode -- legitimate thematic repetition is indistinguishable from coordinated injection using similarity alone. The 0.85 threshold works for a sparse 5-document collection and needs significant recalibration for any corpus with natural document overlap.

The self-healing approach decouples detection from ingestion latency, which solves the performance problem cleanly. The LLM-as-judge variant with domain-specific grounding rules is the more powerful version -- "financial corrections must reference an official URL" is something pure embedding similarity can never enforce.

Honest summary: embedding anomaly detection works well in the specific conditions I tested and breaks down in the conditions you're describing. One signal among several, not a general solution.