AnalysisAI ModelsAugust 24, 2026

In-Cell Learning lets deployed LLMs learn without changing stored bits

Paper proposes "in-cell learning" for bit-identical, revocable updates to quantized LLMs, so a 4-bit release keeps the same file while gaining new knowledge. Authors argue every prior method — full fine-tuning, adapter merging, model editing — replaces the checkpoint and invalidates evaluations and caches tied to those exact bits.

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