arXiv Machine Learning August 25, 2026 By Zifeng Liu, Yaxin Lu, Xuanhan Wu, Zhiyong Du, Yiming Mao, Zhenhe Wang, Wenqi Shi, Zhengkun Jing, Linwei Liu

In-Cell Learning: Deployed Language Models Can Learn New Knowledge Without Changing a Single Stored Bit

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arXiv Machine Learning

Sep 1

In-Cell Learning: Language Models That Update Their Own Weights in Sequence Without Changing the File They Ship

arXiv:2608.20873v3 Announce Type: replace Abstract: A 4-bit quantized weight specifies a rounding cell rather than a single full-precision value. We introduce in-cell learning, a paradigm for writing...

By Zifeng Liu, Yaxin Lu, Xuanhan Wu, Zhiyong Du, Yiming Mao, Zhenhe Wang, Wenqi Shi, Zhengkun Jing, Linwei Liu

llms efficiency

arXiv Machine Learning

Aug 24

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

arXiv:2608.20873v1 Announce Type: new Abstract: Every way of teaching a deployed language model something new -- full fine-tuning, adapter merging, model editing -- replaces the released checkpoint,...

By Zifeng Liu, Zhiyong Du, Yaxin Lu, Yiming Mao, Zhenhe Wang, Wenqi Shi, Zhengkun Jing

llms fine-tuning efficiency

arXiv Machine Learning

Sep 30

Behavioral Capacity Certificates for Quantized Language Models

arXiv:2609.37887v1 Announce Type: new Abstract: Activation and key-value cache precision change what a quantized language model computes without altering its stored weights. Direct weight-code bounds...

By Arian Eamaz, Mojtaba Soltanalian

llms efficiency

arXiv AI

Aug 24

Quantization-Aware Healing: A Practical Recipe for Recovering Compressed, 4-Bit LLMs

arXiv:2608.20953v1 Announce Type: cross Abstract: Serving large language models cheaply increasingly means shipping models that are both structurally compressed to a fraction of their parameters and...

By Bakbergen Ryskulov, Iker Garc\'ia-Ferrero, David Montero, David Jansen, Ali Hashemi, Jezabel R. Garcia, Antonio Tiene, Rom\'an Or\'us

llms efficiency benchmarks

arXiv Computation and Language

Sep 16

Where Post-Training Quantization Breaks Text Embedders: A Measured Map Across Four Embedder Families

arXiv:2609. 16391v1 Announce Type: cross Abstract: Weight-only post-training quantization is the cheapest way to shrink a retrieval embedder, and the received advice for applying it -- protect the embedding table, allocate bits by module sensitivity, prefer a ranking-aware objective over weight reconstruction -- was carried into LLM quantization largely intact.

By Hyojung Han

llms rag efficiency

arXiv Machine Learning

Jul 14

Can a Language Model Learn Facts Continually in Its Weights?

arXiv:2607. 11020v1 Announce Type: cross Abstract: Continual learning promises a language model that keeps acquiring knowledge after training, with each new fact written into its weights.

By Charles O'Neill

llms

One email a morning, machine-written

Related stories

In-Cell Learning: Language Models That Update Their Own Weights in Sequence Without Changing the File They Ship

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

Behavioral Capacity Certificates for Quantized Language Models

Quantization-Aware Healing: A Practical Recipe for Recovering Compressed, 4-Bit LLMs

Where Post-Training Quantization Breaks Text Embedders: A Measured Map Across Four Embedder Families

Can a Language Model Learn Facts Continually in Its Weights?