arXiv AI June 9, 2026 By Encheng Su, Jianyu Wu, Jinouwen Zhang, Qiucheng Yu, Chen Tang, Pengze Li, Lintao Wang, Aoran Wang, Xinzhu Ma, Shixiang Tang, Yizhou Wang, Houqiang Li

S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering

Read the original on arXiv AI →

arXiv:2605. 28831v2 Announce Type: replace-cross Abstract: Long-horizon memory question answering often requires sparse evidence from heterogeneous histories, including events, object states, visual observations, temporal relations, and causal steps.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

Sep 2

EM^2Mem: Event-Centric Multimodal Memory for Large Language Models

arXiv:2609.00551v1 Announce Type: cross Abstract: Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, s...

By Yijun Chen, Yaqi Zheng, Yanya Li, Boyi Xiao, Buqiang Xu, Shuofei Qiao, Jizhan Fang, Xinle Deng, Yunzhi Yao, Xuehai Wang, Liuxin Zhang, Hui Li, Huajun Chen, Shumin Deng

llms nlp multimodal benchmarks

arXiv AI

Aug 11

SodaMem: Evidence-Grounded Temporal Graph Memory for LLM Agents

arXiv:2608. 08055v1 Announce Type: new Abstract: Large language model (LLM) agents that assist users over weeks of conversation must remember what is currently true, not merely what was once said.

By Fengrong Wan, Chengcan Wu, Ningtao Lyu

llms rag agents

arXiv Computation and Language

Aug 31

Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering

Entity-Memory graph retrieval preserves dialogue turns as verbatim memory nodes, links repeated mentions via shared entities, and connects adjacent memories with chronological edges. During retrieval, the system gates through entities, fuses semantics, and performs one‑hop chronological recovery before dense backfill, allowing it to keep neighboring memories that dense cosine ranking might miss. On 1,986 questions from ten LoCoMo conversations, this graph retrieval method increases official evidence recall at top‑k 25 from 79.7468 % to 84.4842 %, with the advantage extending from top‑k 5 to 50, though it does not improve overall final‑answer F1.

By Shumao Sun

llms rag nlp

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

arXiv Computation and Language

Sep 15

Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering

arXiv:2609.07093v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely a...

By Yifan Wang, Xinkui Lin, Yongxiu Xu, Shen Gao, Ruochen Yang, Kun Huang, Yubin Wang, Jie Wu, Wei Liu, Jian Luan, Hongbo Xu, Shuo Shang

llms rag nlp benchmarks

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Related stories

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

EM^2Mem: Event-Centric Multimodal Memory for Large Language Models

SodaMem: Evidence-Grounded Temporal Graph Memory for LLM Agents

Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering

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

Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering