SOURCE-LINKED INTELLIGENCE
Quantization-Triggered Backdoors in Language Models: Cross-Quantizer Transferability and the Validation--Deployment Gap
Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision source checkpoint is evaluated and quantization is applied downstream without equivalent re-evaluation, this workflow creates a structural validation--deployment gap: because quantization is a many-to-one mapping over parameter space, source-precision certification does not guarantee behavioral equivalence in the deployed configuration. We formalize this gap through Quantization Behavioral Equivalence Classes (QBECs) and prove that QBEC membersh
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T08:34:10.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.