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Nothing Changed but the Model: CellFill -- Bounded In-Cell Learning for Bit-Identical, Revocable Updates to Quantized LLMs

arXiv cs.LG LLMs & Foundation Models Zifeng Liu, Zhiyong Du, Yaxin Lu, Yiming Mao, Zhenhe Wang, Wenqi Shi, Zhengkun Jing 2026-08-21

TL;DR - CellFill updates quantized LLMs by learning bounded residuals within each weight’s quantization cell, leaving the released 4-bit checkpoint bit-identical after re-quantization. This enables verifiable, exactly revocable knowledge updates while limiting model drift and cross-domain forgetting.

The integer codes and scales remain frozen; removing the learned residual fully revokes an update.
Constrained dense training nearly matched unconstrained fact recall across paired seeds: 58.9% versus 59.3%, a -0.5-point paired difference with 95% CI [-5.0, +4.0].
Cell projection reduced cross-domain forgetting relative to unmerged adapters in every converged run, acting as a trust region rather than fixing unstable training.
The method scaled to a 27B hybrid linear-attention model with 24 billion constrained weights and verified bit-identical re-quantization.

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