The paper introduces QGMem, a memory framework for conversational agents that organizes dialogue history into event-indexed atomic units and consolidates related units into dynamic memory traces. When a query is posed, QGMem retrieves complementary candidate memories, reranks them to form a compact working memory, and represents this working memory as a local graph to expose relational dependencies. Experiments on six benchmarks demonstrate consistent improvements in retrieval, multi‑hop evidence composition, conflict resolution, and ultra‑long dialogue reasoning while keeping context size and inference cost moderate.
By Yichen Liu, Chunfeng Yuan, Haowei Liu, Wenjuan Li, Zefeng Lin, Bing Li, Xu Chen, Weiming Hu
The paper introduces LOCOMO-CONV, a conversational memory benchmark that expands on the existing LoCoMo dataset with four query styles—dialog, implicit, counterfactual, and composed—designed to evaluate memory systems in realistic conversational settings. Experiments across five memory systems reveal that conversational framing uncovers significant retrieval gaps missed by traditional QA benchmarks, particularly for implicit and composed queries, and that strong retrieval does not necessarily translate into higher response quality. The study also highlights silent grounding in implicit queries, where memory enhances contextual grounding without explicitly presenting the gold fact, suggesting a need for reasoning-based memory elaboration.
By Wen-Yu Chang, Yun-Nung Chen
UTILMEM is a new diagnostic benchmark that tests how conversational agents use long‑term memory, focusing on reasoning over dense histories, spotting implicitly relevant memories, synthesizing distributed evidence, and resisting interference from similar distractors. It contains 1,717 instances across five domains and evaluates a range of retrieval‑based and memory‑augmented systems. The study shows that strong performance on traditional factual recall does not guarantee effective memory utilization, highlighting a gap between retrieving information and integrating it into coherent, task‑oriented outputs.
By Peijun Qing, Fobo Shi, Soroush Vosoughi
JustMem is a new memory system for long‑term conversational AI that stores conversation history as compact atomic memories and adapts its access strategy to each query. It introduces two dimensions of memory access—discovery breadth and reading fidelity—implemented through LOOKUP for local evidence, COMPOSE for distributed evidence, and REPLAY for fidelity‑sensitive evidence. Experiments on LoCoMo and LongMemEval‑S show that JustMem outperforms competing memory systems in accuracy and recall while using fewer generative‑model tokens for memory construction and inference.
By Guanhua Chen, Yanting Wang, Wenjing Zhi, Lei Sha
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
arXiv:2605.01688v2 Announce Type: replace-cross
Abstract: Long-horizon memory systems increasingly improve how evidence is stored and retrieved, yet the generator must still reason over fragments who...
By Yushi Sun, Bowen Cao, Dong Fang, Lingfeng Su, Wai Lam
arXiv:2609.21940v1 Announce Type: new
Abstract: Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions. Existing me...
By Zijie Cao, Xijun Qu, Zhicheng Gu, Xiaoshu Chen, Duanyang Yuan, Yanning Hou, Sihang Zhou, Jianxing Gong, Jian Huang, Yang Mei
DiaLong is a new long‑context dialogue benchmark that tests transformer‑based Large Language Models’ ability to retain and recall memories. The dataset contains 100 human‑created conversations, expanded with LLMs to exceed lengths of existing datasets, and includes a fact‑recall task where models must classify 40 candidate facts as true or false. Experiments show current LLMs and memory‑enhancement methods still struggle, indicating a need for improved memory performance in extended conversations.
By Owen Bianchi, Samer Aslan, Ryunosuke Saito
The paper introduces Temporal Semantic Memory (TSM), a framework that improves how large language model agents manage memory by addressing two key shortcomings: temporal inaccuracy and temporal fragmentation. TSM constructs a semantic timeline instead of a dialogue timeline, consolidating temporally continuous and semantically related information into durative memory. During retrieval, it aligns the query’s temporal intent with the semantic timeline, enabling the use of temporally appropriate durative memories and yielding up to a 12.2% accuracy boost over existing methods.
By Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
Large language model agents are increasingly deployed in human-human interaction settings, such as meeting assistants and clinical documentation systems, where they must observe conversations and retain information for downstream queries. Unlike traditional human-assistant settings, these environments are inherently multimodal, involve complex discourse phenomena such as anaphora and deixis, and contain asynchronous or conflicting information from multiple participants.
CueMem is a cue‑guided framework for long‑term conversational memory that reconstructs query‑relevant dialogue context from compressed memory records. Instead of treating memory units as self‑contained evidence, it extracts fine‑grained cues linked to their source turns and, at query time, expands from these cues over a turn graph to rebuild a compact evidence context. Experiments on LoCoMo and LongMemEval show that CueMem outperforms baseline memory methods, reduces input tokens and latency, and improves long‑term conversational question answering.
By Changjian Wang, Rongzhen Li, Weili Guan, Shuming Shi, Quan Lu, Ning Jiang
arXiv:2609.26780v1 Announce Type: cross
Abstract: Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distingu...
By Haobo Zheng, Tan Tang, Yan Chen, Weijie Wang, Yingcai Wu