arXiv AI

MoNe: Modular Neural Memory for Efficient Long Context Inference

arXiv:2608. 17616v1 Announce Type: new Abstract: We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining.

arXiv AI

Jun 2

Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference

arXiv:2603. 29002v3 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning.

By Zifan He, Rui Ma, Yizhou Sun, Jason Cong

llms rag

arXiv AI

Jun 9

FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention

arXiv:2606. 09079v1 Announce Type: cross Abstract: Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving.

By Yan Wang, Qifan Zhang, Jiachen Yu, Tian Liang, Dongyang Ma, Xiang Hu, Zibo Lin, Chunyang Li, Zhichao Wang, Jia Li, Yujiu Yang, Haitao Mi, Dong Yu

llms efficiency

arXiv Machine Learning

Jul 13

Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking

arXiv:2602. 21196v2 Announce Type: replace Abstract: Efficiently processing long sequences with Transformer models usually requires splitting the computations across accelerators via context parallelism.

By Ravi Ghadia, Maksim Abraham, Sergei Vorobyov, Max Ryabinin

llms rag efficiency

arXiv AI

Jun 9

End-to-End Context Compression at Scale

arXiv:2606. 09659v1 Announce Type: cross Abstract: Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length.

By Ang Li, Sean McLeish, Haozhe Chen, Nimit Kalra, Zaiqian Chen, Artem Gazizov, Venkata Anoop Suhas Kumar Morisetty, Bhavya Kailkhura, Harshitha Menon, Zhuang Liu, Brian R. Bartoldson, Tom Goldstein, Sanae Lotfi, Micah Goldblum, Pavel Izmailov

llms rag agents efficiency

Hugging Face Trending Papers

Jun 8

End-to-End Context Compression at Scale

Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degrade model quality substantially or require considerable time and compute to compress a single long prompt.

llms rag agents efficiency

arXiv AI

Jun 16

Long-Context Modeling via GSS-Transformer Hybrid Architecture with Learnable Mixing

arXiv:2606. 16093v1 Announce Type: cross Abstract: Modeling long-range dependencies remains a central challenge in natural language processing.

By Kuzey Torlak, H\"useyin Arda Arslan, An{\i}l Dervi\c{s}o\u{g}lu, Beyza Nur Deniz, Onur Boyar

llms nlp efficiency

arXiv Machine Learning

Jul 7

Learning When to Attend: Conditional Memory Access for Long-Context LLMs

arXiv:2603. 17484v2 Announce Type: replace-cross Abstract: Language models struggle to generalize beyond pretraining context lengths, limiting long-horizon reasoning and retrieval.

By Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato, Elvis Nunez, Matthew Trager, Wei Xia, Stefano Soatto

llms efficiency

arXiv AI

Jul 14

Extending LLM Context via Associative Recurrent Memory

arXiv:2607. 11614v1 Announce Type: cross Abstract: Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and linear memory scaling.

By Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov, Yuri Kuratov, Lyudmila Rvanova, Mikhail Katkov, Ilia Sochenkov, Misha Tsodyks, Timothy Baldwin, Mikhail Burtsev, Artem Shelmanov

llms fine-tuning

arXiv Machine Learning

Jul 14

Controllably Efficient Language Models

arXiv:2511. 05313v2 Announce Type: replace Abstract: The substantial inference costs of attention in transformers motivated the development of efficient sequence mixers: namely sparse and sliding window attention, convolutions and linear attention.

By Jatin Prakash, Aahlad Puli, Rajesh Ranganath

llms efficiency benchmarks

arXiv Machine Learning

Jul 17

LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget

arXiv:2607. 14952v1 Announce Type: new Abstract: A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment.

By Changhai Zhou, Kieran Liu, Yuhua Zhou, Qian Qiao, Jun Gao, Harry Zhang, Irvine Lu, Nolan Ho, Lucian Li, Andrew Lei, Cleon Cheng, Steven Chiang, Yihang Zeng, Di Zhang, Rio Yang, Kaijie Chen, Andrew Chen, Pony Ma, Weizhong Zhang, Cheng Jin

agents reinforcement-learning

arXiv AI

Jul 3

Mixture-of-Parallelisms: Towards Memory-Efficient Training Stack for Mixture-of-Experts Models

arXiv:2607. 01844v1 Announce Type: cross Abstract: This paper showcases a memory-efficient training stack for Mixture-of-Experts (MoE) models.

By Xuan-Phi Nguyen, Shrey Pandit, Yiran Zhao, Semih Yavuz, Silvio Savarese, Shafiq Joty

fine-tuning efficiency benchmarks

Hugging Face Trending Papers

Jul 30

Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory

Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independently. Memory Decoder introduces a parametric long-term memory module but only studies it at a relatively small scale.

llms benchmarks

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