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

2026-08-21

AI Summary

研究解决了更新量化语言模型时破坏原有评估和缓存的问题，通过在解量化间隙中学习新知识而不改变原权重位，确保更新可撤销且漂移受限。

Abstract

Every way of teaching a deployed language model something new -- full fine-tuning, adapter merging, model editing -- replaces the released checkpoint, and with it every evaluation and cache that referred to those exact bits. We instead learn inside the dequantization gap: with the integer codes and scales of a 4-bit release frozen, new knowledge is written only into the per-weight residual that lives strictly inside each quantization decision cell. Re-quantization then returns the released artifact bit-for-bit, a machine-checkable guarantee; updates are exactly revocable by dropping the residual; and drift is bounded. We give six propositions and three training paths, including CellFill, a bounded reparameterization that makes invariance structural rather than enforced. Exact invariance turns out to be nearly free: across three paired seeds the constrained dense path matches an unconstrained reference whose weights provably escape the artifact (58.9 vs 59.3 percent fact recall; paired difference -0.5 points, 95% CI [-5.0,+4.0]), and is better on held-out cross-domain perplexity. Against the natural null hypothesis -- serving the same update as an unmerged adapter -- projecting into the cells reduces cross-domain forgetting in every run that converged, and a diverged control shows the boundary: projection is a trust region, not a repair. What no method escapes is the cost of knowledge itself, and the apparent free lunch of in-domain perplexity improving past the anchor is an artifact of rehearsal sharing a corpus with the metric. Methods differ threefold at matched rehearsal in knowledge bought per point of cross-domain perplexity, a ranking that is not the recall ranking. The method transfers to a 27B hybrid linear-attention model (2.4e10 constrained weights, verified bit-identical), where matched recall costs about half as much cross-domain perplexity as at 1.7B.

Problem

• language model
• quantization
• in-cell learning
• bit-identical
• revocable updates

Innovation

• CellFill
• In-Cell Learning
• Quantized LLMs
• Bit-Identical Updates