arXiv AI

DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning

arXiv:2606. 15778v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining.

arXiv AI

Jul 14

UNIT: Unleash Large Language Models Potential for Graph Continual Learning

arXiv:2607. 10159v1 Announce Type: new Abstract: In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures.

By Tairan Huang, Yili Wang, Beibei Hu, Yiting Shi, Qiutong Li, Changlong He, Jianliang Gao

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 14

CA-DGCL: Dynamic Graph Continual Learning via Condensation and Attachment

arXiv:2607. 11112v1 Announce Type: new Abstract: Dynamic graph continual learning (DGCL) is an effective manner for handling catastrophic forgetting in dynamic graphs.

By Tingxu Yan Ye Yuan

arXiv AI

Jul 16

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.

By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu

llms agents reinforcement-learning benchmarks

arXiv AI

6d ago

EvoGraph-Mem: Failure-Aware Editable Graph Memory for Long-Term Language Agents

arXiv:2608. 11248v1 Announce Type: new Abstract: Long-term memory is essential for language agents operating across extended interactions and evolving tasks.

By Yuxi Qian, Yuxiang Ren

agents benchmarks

Hugging Face Trending Papers

Jul 15

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 11

Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages

arXiv:2603. 12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting, a critical limitation of the static pre-training paradigm inherent to modern LLMs.

By Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin

llms fine-tuning benchmarks safety

arXiv AI

Jul 31

A Graph-Native Bitemporal Memory Store for Conversational AI Agents

arXiv:2607. 26520v1 Announce Type: cross Abstract: Conversational AI agents commonly lack persistent memory across sessions.

By Alp Niksarli, Gopesh Baheti

llms rag agents benchmarks

arXiv AI

Jun 3

MemVerse: Multimodal Memory for Lifelong Learning Agents

arXiv:2512. 03627v2 Announce Type: replace Abstract: Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember.

By Junming Liu, Yifei Sun, Weihua Cheng, Haodong Lei, Yirong Chen, Licheng Wen, Xuemeng Yang, Daocheng Fu, Pinlong Cai, Nianchen Deng, Yi Yu, Shuyue Hu, Botian Shi, Ding Wang

agents efficiency multimodal safety

arXiv AI

Jul 24

CAMeR: Keyword-Gated Hybrid Activation for Adaptive Memory Retention in LLM Agents

arXiv:2607. 20458v1 Announce Type: cross Abstract: Large language model (LLM) agents operating over extended dialogues accumulate vast amounts of information, yet existing memory systems either retain everything indiscriminately or apply uniform forgetting heuristics that fail to distinguish relevant from irrelevant knowledge.

By Haowen Lai

llms rag agents benchmarks

arXiv AI

Jun 3

DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees

arXiv:2606. 03083v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions.

By Haoran Tan, Zeyu Zhang, Zhicheng Cao, Rui Li, Xu Chen

llms agents

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

Hugging Face Trending Papers

Jul 29

A Graph-Native Bitemporal Memory Store for Conversational AI Agents

Conversational AI agents commonly lack persistent memory across sessions. The obvious fixes like injecting full chat histories into the context window, or delegating to a third-party memory service, either exhaust the model's context budget or send personal data through infrastructure the user does not control.

llms rag agents benchmarks

Related stories