arXiv AI June 26, 2026 By Sayak Dutta

CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention

arXiv:2606. 27229v1 Announce Type: cross Abstract: Recurrent models must forget in order to remember, yet the state of the art decides what to erase without consulting what is stored -- the gate sees only the arriving token, not the memory it is about to modify.

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

Jul 15

Forgetful Attention: A Trainable Support-Vector Memory with Certified Selection and Exact Unlearning

arXiv:2607. 12204v1 Announce Type: new Abstract: Attention can be viewed as an online learner over context, yet existing test-time memories cannot certify that dropping a token leaves outputs unchanged or delete its influence outright.

By Vishwajith Ramesh

llms rag

arXiv Machine Learning

Jun 2

Don't Read Everything: A Curvature-Conditioned Query for Linear Attention

arXiv:2606. 01294v1 Announce Type: cross Abstract: Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context retrieval and long-context tasks.

By Dong Le, Thong Nguyen, Cong-Duy Nguyen, Anh Tuan Luu

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

Sep 16

Thinking Deeper, Not Longer: Memory-Efficient Test-Time Reasoning with Depth-Recurrent Transformers for Compositional Generalization

arXiv:2603.21676v2 Announce Type: replace-cross Abstract: Standard Transformers have a fixed computational depth, limiting their ability to generalize to tasks that require variable-depth reasoning....

By Hung-Hsuan Chen

llms safety

arXiv AI

Aug 7

GROM: Gradient-Free Rapid One-Shot Machine Unlearning

arXiv:2608. 05783v1 Announce Type: cross Abstract: Machine unlearning has become a critical capability for safely removing specific, sensitive knowledge from large language models (LLMs).

By Pawe{\l} Batorski, Przemys{\l}aw Spurek, Paul Swoboda

llms fine-tuning efficiency benchmarks

arXiv AI

Sep 1

DASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving

arXiv:2608.30386v1 Announce Type: cross Abstract: Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substa...

By Yanqi Yu, Pingwei Sun, Jianchao Tan, Tao Zhang, Yuchen Xie, Xunliang Cai, Yao Liu

efficiency benchmarks

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Related stories

Forgetful Attention: A Trainable Support-Vector Memory with Certified Selection and Exact Unlearning

Don't Read Everything: A Curvature-Conditioned Query for Linear Attention

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

Thinking Deeper, Not Longer: Memory-Efficient Test-Time Reasoning with Depth-Recurrent Transformers for Compositional Generalization

GROM: Gradient-Free Rapid One-Shot Machine Unlearning

DASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving