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Proposes in-cell learning for bit-identical, revocable updates to quantized LLMs, introducing the CellFill algorithm to update models within quantization cells without altering the original artifact.
This paper investigates whether open-source quantized LLMs encode a linearly separable truthfulness signal in their hidden states. Across three 7B-8B instruction-tuned models, a linear probe on a single mid-network layer achieves 0.904-1.000 AUROC on hallucination detection benchmarks, outperforming sampling-based methods.