The paper introduces T‑Mem, a memory framework that uses temporal structure to manage long‑horizon multi‑party conversations. It employs Temporal Evidence Discovery to find locally concentrated evidence, Temporal Memory Structuring to model state transitions and compress low‑information spans, and Evidence Forest Linearization to serialize the evidence for answer generation. Experiments on two benchmarks show that T‑Mem outperforms existing memory frameworks across various settings.
By Junyou Guo, Chongyang Tao, Yan He, Yong Wang, Shuai Ma
arXiv:2606. 04555v1 Announce Type: cross Abstract: Long-horizon conversational agents need to interact with users through evolving events, tasks, and goals.
By Yifan Simon Liu, Liam Gallagher, Faeze Moradi Kalarde, Jiazhou Liang, Armin Toroghi, Scott Sanner
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: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
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
Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time.