arXiv AI

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.

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

arXiv AI

Jun 30

Mandol: An Agglomerative Agent Memory System for Long-Term Conversations

arXiv:2606. 29778v1 Announce Type: cross Abstract: Long-term conversational agents need to remember and query cross-session, multi-typed information with complex correlations.

By Yuhan Zhang (Institute of Software, Chinese Academy of Sciences), Zhiyuan Guo (Institute of Software, Chinese Academy of Sciences), Ziheng Zeng (Institute of Software, Chinese Academy of Sciences), Wei Wang (Institute of Software, Chinese Academy of Sciences), Wentao Wu (Microsoft Research), Lijie Xu (Institute of Software, Chinese Academy of Sciences)

llms rag agents benchmarks

arXiv AI

Jun 4

MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning

arXiv:2606. 04442v1 Announce Type: cross Abstract: AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents.

By Qiyang Xie, Jialun Wu, Xinjie He, Su Liu, Shuai Xiao, Zhiyuan Lin, Weikai Zhou

llms rag benchmarks

Hugging Face Trending Papers

Jun 3

MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning

AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously.

llms rag benchmarks

arXiv AI

Jun 16

T-Mem: Memory That Anticipates, Not Archives

arXiv:2606. 15405v1 Announce Type: cross Abstract: Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many sessions earlier, and adapt their behaviour to each user.

By Weidong Guo, Dakai Wang, Zixuan Wang, Hui Liu, Yu Xu

llms agents benchmarks

arXiv AI

Jun 15

VikingMem: A Memory Base Management System for Stateful LLM-based Applications

arXiv:2605. 29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions.

By Jiajie Fu, Junwen Chen, Mengzhao Wang, Aoxiang He, Maojia Sheng, Xiangyu Ke, Yifan Zhu, Yunjun Gao

llms agents benchmarks

arXiv AI

Jul 31

IFCMemoryBench: Evaluating Long-Term Memory of LLM-Based Agents in BIM Information Retrieval

arXiv:2607. 26072v1 Announce Type: cross Abstract: Long-term memory is becoming a core capability of LLM-based agents, but existing evaluations largely test conversational recall in open-domain or persona-grounded settings.

By Changyu Du, Alexander Vosseler, Filippo Mazza, Andr\'e Borrmann

llms agents benchmarks

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

Jun 6

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

arXiv:2606. 06337v1 Announce Type: new Abstract: Large language model (LLM) deployments for long-horizon tasks face a fundamental constraint: context windows are finite while productive work sessions are not.

By Shweta Mishra

llms benchmarks

arXiv AI

Jul 21

Accurate and Efficient Long-Term Memory for LLM Agents

arXiv:2607. 16211v1 Announce Type: new Abstract: LLM agents augmented with persistent memory can recall past interactions, but existing systems suffer from two limitations: flat, unstructured storage loses relational context needed for multi-hop and temporal reasoning, and reliance on expensive LLM-based classification makes them impractical for latency-sensitive deployment.

By Zicheng Zhao, Xinyang Guo, Luyao Lv, Menghan Wang, Ming Li, Shuaicheng Li

llms agents

arXiv AI

Jun 6

SubtleMemory: A Benchmark for Fine-Grained Relational Memory Discrimination in Long-Horizon AI Agents

arXiv:2606. 05761v1 Announce Type: new Abstract: Persistent AI assistants, such as OpenClaw, accumulate large collections of related memories over long-term interactions.

By Wenxuan Wang, Haoyu Sun, Fukuan Hou, Mingyang Song, Weinan Zhang, Yu Cheng, Yang Yang

agents 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

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