arXiv Machine Learning August 24, 2026 By Zifeng Liu, Zhiyong Du, Yaxin Lu, Yiming Mao, Zhenhe Wang, Wenqi Shi, Zhengkun Jing

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

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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 Machine Learning

1d ago

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

arXiv:2608.20873v2 Announce Type: new Abstract: A deployed language model is a file that many things depend on - a benchmark report, a certification, a fleet of devices - and every way of teaching it...

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

llms benchmarks

arXiv Machine Learning

6d ago

Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs

The study investigates how post‑training quantization (PTQ) affects proactive interference (PI) in large language models. Using bitsandbytes, the authors compare FP16, INT8, and INT4/NF4 precision across three instruction‑tuned models and find that INT4 quantization markedly degrades accuracy under high interference, with INT8 also incurring a smaller penalty in two of the three models. The degradation is linked to increased same‑key intrusion errors and originates in the quantized transformer backbone rather than the output layer.

By Shayan Shahrabi-Farahani (Shahid Beheshti University, Tehran, Iran), Dara Rahmati (Shahid Beheshti University, Tehran, Iran)

llms nlp efficiency benchmarks

arXiv AI

Jun 12

TimeROME-DLM: Temporal Causal Tracing and Low-Rank Inference-Time Knowledge Editing for Masked Diffusion Language Models

arXiv:2606. 12841v1 Announce Type: cross Abstract: Masked diffusion language models (MDLMs) such as LLaDA now rival autoregressive (AR) LLMs, but every existing knowledge-editing and unlearning method (ROME, MEMIT, etc.

By Zhengtao Yao, Liuyang Song, Hongbo Zhang, Chenhao Wei, Haoyan Xu, Guang Yang, Siheng Wang

llms diffusion

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 AI

1d ago

Crossing the Margin Cliff: Toward Relearn-Robust LLM Unlearning via Margin Calibration

arXiv:2607.27836v2 Announce Type: replace Abstract: Large language model unlearning is consistently fragile under relearn attacks. On TOFU, fine-tuning on twenty forget examples substantially recover...

By Xiangyu Yin, Jiaxu Liu, Zhen Chen, Chih-Hong Cheng

llms fine-tuning efficiency

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Related stories

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

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

Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs

TimeROME-DLM: Temporal Causal Tracing and Low-Rank Inference-Time Knowledge Editing for Masked Diffusion Language Models

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

Crossing the Margin Cliff: Toward Relearn-Robust LLM Unlearning via Margin Calibration