arXiv AI

ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents

ReMem is a new recommendation agent framework that rethinks perception and memory for long-context recommendation tasks. It replaces raw HTML parsing with OCR-based multimodal perception from screenshots, extracting structured information in a platform-agnostic way. The framework also introduces a chunk-wise sequential memory update strategy and a multi-memory GRPO variant to efficiently model evolving user preferences over arbitrarily long interaction histories, achieving a 5.16% average improvement over state-of-the-art baselines on three recommendation agent tasks.

arXiv AI

Aug 28

When Memory Takes Gradients: Collaborative Vector Memory for Agentic Recommender Systems

The paper introduces CoVeMem, a Collaborative Vector Memory system that replaces text-based memory in agentic recommender systems with vectorized user and item states derived from a frozen LightGCN model. By retrieving relevant historical states at each decision and integrating them as soft tokens in the LLM’s context, CoVeMem enables contrastive alignment and listwise co‑training to learn how to read and rank these states, achieving performance on par with or better than existing text‑memory agents across multiple benchmarks without extra LLM calls for memory updates.

By Hanchong Chen, Xing Tang, Lingjie Li, Xiongfeng Shan, Xiuqiang He

llms agents benchmarks safety

arXiv AI

Sep 16

ThinkFlow: Self-Evolving Probabilistic Latent Memory for Lifelong Conversational Agents

arXiv:2609.17010v1 Announce Type: new Abstract: Lifelong conversational agents rely on memory systems to maintain deep, context-aware interactions with users. However, existing explicit textual memor...

By Cai Ke, Xin Liu, Han Zhang, Jiangyue Yan, Zike Yuan, Ling Deng, Yue Yu, Hui Wang, Ruifeng Xu

agents diffusion benchmarks safety

arXiv AI

Jun 11

MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback

arXiv:2606. 07909v2 Announce Type: replace Abstract: Modern large language model (LLM) agents can use external tools to help users solve complex tasks.

By Suleyman Armagan Er, Danilo Ribeiro, Yogesh Virkar, Surafel Lakew, Adi Kalyanpur, James Gung, Thomas Delteil, Arshit Gupta

llms agents fine-tuning benchmarks

arXiv AI

Jun 30

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation

arXiv:2606. 28357v1 Announce Type: cross Abstract: Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.

By Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Frank Shyu, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

agents fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 12

MemRefine: LLM-Guided Compression for Long-Term Agent Memory

arXiv:2606. 13177v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks.

By Minjae Kim, Jinheon Baek, Soyeong Jeong, Sung Ju Hwang

llms agents benchmarks

arXiv AI

Jul 10

Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing

arXiv:2607. 08497v1 Announce Type: cross Abstract: Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing.

By Feng Wang, Canmiao Fu, Zhipeng Huang, Chen Li, Jing Lyu, Ge Li

llms agents diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Aug 5

LeanMem: Simple and Efficient Long-Term Memory for LLM Agents

arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.

By Yuxin Liao, Le Wu, Min Hou, Hao Liu, Han Wu, Zishu Wang

llms agents nlp

arXiv AI

Jul 21

Mechanistic Attention Guidance for Agent Memory Refinement

arXiv:2607. 17621v1 Announce Type: new Abstract: Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections.

By Yechao Hong, Haiquan Qiu, Yaqing Wang, Quanming Yao

agents benchmarks

arXiv AI

Jun 9

MemToolAgent overview with a simple restaurant booking scenario where the agent retrieves similar memories, receives feedback on an invalid time format, and generates a reflection to update its memory

arXiv:2606. 07909v1 Announce Type: new Abstract: Modern large language model (LLM) agents can use external tools to help users solve complex tasks.

By Suleyman Armagan Er, Danilo Ribeiro, Yogesh Virkar, Surafel Lakew, Adi Kalyanpur, James Gung, Thomas Delteil, Arshit Gupta

llms agents fine-tuning benchmarks

arXiv AI

Aug 18

QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents

arXiv:2608. 16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence.

By Heng Wang, Yifei Li, Lingling Zhang, Pengyu Li, Xinyu Che, Xinyu Zhang, Zesheng Yang

llms agents benchmarks

Hugging Face Trending Papers

Jun 11

MemRefine: LLM-Guided Compression for Long-Term Agent Memory

Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks. However, as interactions accumulate, the memory store grows without bound and fills with redundant entries that inflate storage cost and degrade retrieval by crowding out the most useful evidence.

llms agents benchmarks

arXiv AI

Jun 30

Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

arXiv:2507. 05257v4 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, and retrieve long-term information-is under-evaluated due to the lack of benchmarks.

By Yuanzhe Hu, Yu Wang, Julian McAuley

llms rag agents benchmarks

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