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

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

arXiv Machine Learning

2d ago

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 AI

2d ago

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 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

arXiv AI

Jul 24

Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing

arXiv:2607. 20433v1 Announce Type: cross Abstract: While language models remain frozen at their training state, the world evolves continuously.

By Jea Kwon, Jiwon Kim, Dong-kyum Kim, Meeyoung Cha

llms reinforcement-learning fine-tuning

arXiv AI

Jun 29

Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents

arXiv:2606. 27472v1 Announce Type: cross Abstract: Large language model (LLM) agents operate over long, multi-session interactions in which facts change: a user moves, a price updates, a plan is revised.

By Vedant Patel

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 7

LGQ: Learnable Geometric Quantization for Image Tokenization

arXiv:2602. 16086v3 Announce Type: replace-cross Abstract: Recent collapse-free quantizers such as FSQ achieve stable training by replacing the learnable codebook with an engineered geometry: a fixed scalar grid whose structure is dictated by the codebook size K.

By Idil Bilge Altun, Mert Onur Cakiroglu, Elham Buxton, Mehmet Dalkilic, Hasan Kurban

diffusion nlp efficiency benchmarks

One email a morning, machine-written

Related stories

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

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

Can a Language Model Learn Facts Continually in Its Weights?

Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing

Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents

LGQ: Learnable Geometric Quantization for Image Tokenization