Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models explores A tuning-free runtime monitor that detects LLM misbehavior like backdoors, jailbreaks, and prompt injections using inter-layer hidden-state trajectories.. Commercial viability score: 7/10 in LLM Runtime Safety.
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/buildability/layerwise-convergence-fingerprints-for-runtime-misbehavior-detection-in-large-language-models
Subject: Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models
Verdict
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Receipt path
/buildability/layerwise-convergence-fingerprints-for-runtime-misbehavior-detection-in-large-language-models
Paper ref
layerwise-convergence-fingerprints-for-runtime-misbehavior-detection-in-large-language-models
arXiv id
2604.24542
Generated at
2026-04-28T15:17:47.756Z
Evidence freshness
fresh
Last verification
2026-04-28T15:17:47.756Z
Sources
4
References
0
Coverage
67%
Lineage hash
d68b92bff3fff8d5688a8e92a9d1ecfba745996560ec6b9dc78ff46a312fddcc
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Pending verification refs / 4 sources / Verification pending
references
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Dimensions overall score 7.0
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This equation captures one of the core mathematical components of the system. he representation velocity at layer ℓas the inter-layer difference: δℓ= hℓ+1 −hℓ∈Rd, (1) where d is the hidden dimension
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