arXiv AI June 12, 2026 By Zhengtao Yao, Liuyang Song, Hongbo Zhang, Chenhao Wei, Haoyan Xu, Guang Yang, Siheng Wang

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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 Computation and Language

Sep 3

Knowledge Editing for Masked Diffusion Language Models

The paper investigates whether the locate‑then‑edit approach for knowledge editing, previously applied only to autoregressive language models, can be transferred to masked diffusion models (MDMs). It finds that the optimal edit location—an early‑to‑mid‑layer MLP at the last subject token—remains the same for both model types, but that MDMs suffer a sharper decline in performance when editing longer, multi‑token facts. By incorporating intermediate partially‑unmasked states into the edit optimization, the authors restore multi‑token editing performance in MDMs.

By Haewon Park, Yohan Jo

llms diffusion

arXiv AI

Jul 14

MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models

arXiv:2603. 01331v3 Announce Type: replace-cross Abstract: Discrete diffusion language models (dLLMs) generate text by iteratively denoising a masked sequence.

By Kejing Xia, Mingzhe Li, Lixuan Wei, Zhenbang Du, Xiangchi Yuan, Dachuan Shi, Qirui Jin, Wenke Lee

llms diffusion benchmarks

arXiv AI

Jul 28

ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control

arXiv:2607. 22962v1 Announce Type: new Abstract: LLM agents that operate over many turns accumulate facts in an external memory store and reuse them as premises for downstream reasoning.

By Yan Zhang, Shibo Li

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jul 15

Inference-Time Machine Unlearning via Gated Activation Redirection

arXiv:2605. 12765v3 Announce Type: replace Abstract: Large Language Models memorize vast amounts of training data, raising concerns regarding privacy, copyright infringement, and safety.

By Vin\'icius Conte Turani, Ot\'avio Parraga, Jo\~ao Vitor Boer Abitante, Kristen K. Arguello, Joana Pasquali, Ramiro N. Barros, Flavio du Pin Calmon, Christian Mattjie, Rodrigo C. Barros, Lucas S. Kupssinsk\"u

llms efficiency safety

arXiv AI

Jul 14

PRISM Edit: One Vector for All Temporal Answers

arXiv:2607. 11327v1 Announce Type: cross Abstract: Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not always a replacement.

By Chen Huang (Tsinghua University), Qi Zheng (Tsinghua University), Ruiqin Zheng (ByteDance), Long Zeng (Tsinghua University), Yuantong Xu (ByteDance)

llms benchmarks

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Related stories

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

Knowledge Editing for Masked Diffusion Language Models

MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models

ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control

Inference-Time Machine Unlearning via Gated Activation Redirection

PRISM Edit: One Vector for All Temporal Answers