arXiv AI June 11, 2026 By Zhirui Chen, Ziwei Chen, Ling Shao

Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning

arXiv:2606. 11853v1 Announce Type: cross Abstract: Multi-modal large language models (MLLMs) depend on in-context learning (ICL) for rapid task adaptation, but their scalability is severely limited by finite context windows and the growing cost of key-value (KV) caches in long multi-modal sequences.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 29

Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding

arXiv:2607. 24794v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding.

By Linghao Meng, Qiankun Li, Junyuan Mao, Pujin Liao, Zhicheng He, Enbo Zhang, Kun Wang, Yang Liu, Huazhu Fu, Yueming Jin

llms multimodal benchmarks safety

arXiv AI

Aug 11

Keep It Simple: Multi-Key Episodic Memory Retrieval for Ultra-Long Video Understanding

arXiv:2608. 07663v1 Announce Type: cross Abstract: When videos extend from hours to days, directly processing them end-to-end becomes impractical for current Multi-modal Large Language Models (MLLMs).

By Yeeun Choi, Youngbeom Yoo, Joon-Young Lee, Hyolim Kang, Seon Joo Kim

llms agents multimodal benchmarks

arXiv Machine Learning

5d ago

MARCH: Scaling Recurrent Memory with Content-Routed State Anchors

arXiv:2608. 12435v1 Announce Type: new Abstract: Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length.

By Ming Zhang, Kaisen Yang, Shu Yu, Ermo Hua, Ning Ding, Xia Hu, Bowen Zhou, Chaochao Lu, Youbang Sun

llms

arXiv Machine Learning

Jun 25

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

arXiv:2511. 11421v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge.

By Lan Li, Tao Hu, Da-Wei Zhou, Jia-Qi Yang, Han-Jia Ye, De-Chuan Zhan

llms multimodal benchmarks

arXiv AI

Jun 30

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers

arXiv:2606. 29844v1 Announce Type: cross Abstract: The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particularly in long-context scenarios.

By Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin Xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui

llms

arXiv AI

Jun 12

ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding

arXiv:2506. 01274v2 Announce Type: replace-cross Abstract: Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to video understanding remains constrained by suboptimal frame selection strategies, albeit with the rapid development of video-specialized LMMs.

By Hosu Lee, Junho Kim, Hyunjun Kim, Yong Man Ro

llms reinforcement-learning multimodal benchmarks

Related stories

Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding

Keep It Simple: Multi-Key Episodic Memory Retrieval for Ultra-Long Video Understanding

MARCH: Scaling Recurrent Memory with Content-Routed State Anchors

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers

ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding