The paper introduces Temporal State Tracking Memory (TEM), a training‑free framework that stores facts as versioned records linked to their sources and retrieves the relevant versions in source order, labeling historical records before passing them to the answer model. TEM incorporates event memory and original dialogue turns to fill gaps in structured facts. Evaluations on 420 questions from 600 sessions with 60 personas show high key‑entity hit rates (75.71–97.62%) and significant improvements when historical labels are added, while keeping evidence compact (94–118 tokens per answer).
By Pengfeng Song, Yunkai Zhuang, Liye Shi
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
arXiv:2608. 04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons.
By Ben Wang, Kang Zhou, Lifan Guo, Feng Chen, Chi Zhang
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
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.
arXiv:2608.29066v1 Announce Type: cross
Abstract: Stance detection is crucial for understanding the underlying attitude of an expression towards a target. Conversational stance detection is a more ch...
By Yifan Xiang, Bin Liang, Yuqi Huang, Ruifeng Xu, Kam-Fai Wong
The paper introduces Chain on Demand (CoD), a query‑time evidence organization framework that builds a temporary evidence chain from an unchanged turn‑level memory. CoD allocates a fixed anchor budget based on query relevance, lexical diversity, and new query terms, then restores each anchor’s same‑session neighborhood deterministically. In evaluations on LoCoMo and LongMemEval‑S, CoD achieves high accuracy, demonstrating that long‑term conversational memory can remain unchanged while deferring evidence organization until the query is known.
By Di Lu, Zechen Li, Hao Xue
arXiv:2609.23465v1 Announce Type: new
Abstract: Long-horizon conversational memory is especially challenging in multi-actor settings, where relevant evidence is distributed across participants and co...
By Zihao Lu, Zhihang Yuan, Lei Shi
ETAMem is a multi‑view memory framework designed to improve long‑term conversational agents by decoupling heterogeneous information and aligning memory representations with distinct characteristics. It parses conversational data into atomic units, identifies affective content, and organizes these units into three complementary hierarchical views—Event, Affect, and Time—using adaptively calibrated thresholds. Retrieval is performed via entropy‑based allocation of query budgets across views, enabling hierarchical search within each view. Experiments on LoCoMo and LongMemEval‑S show that ETAMem surpasses state‑of‑the‑art baselines with relative accuracy gains of 12.46% and 7.96%, while remaining token‑efficient, and analyses confirm the complementary benefits of the different memory views.
By Xinying Qian, Ying Zhang, Yu Zhao, Xubo Liu, Baohang Zhou, Jeff Z. Pan
APDMem is a hierarchical long‑term memory system for personalized LLM assistants that uses progressive disclosure to retrieve conversation history. It organizes memory into four layers—thematic summaries, personalized key facts, turn‑level evidence notes, and raw messages—and a controller reads from the top layer, drilling down only when necessary. This approach balances cost and fidelity, allowing simple queries to finish early while deeper inspection is triggered for complex or exact‑evidence requests, and a note synthesizer structures retrieved evidence before answer generation. Experiments on LongMemEval show APDMem performs strongly while accessing only 8% of the total conversations.
By Chin-Lun Fu, Anagha Kulkarni, Hong Ni, Behrouz Madahian
The paper introduces Delta-Consistent Scoring (DCS), an annotation‑free method that uses large language model representations to produce continuous monetary‑policy stance scores. DCS jointly learns absolute stance for each FOMC statement and relative shifts between consecutive statements, enforcing consistency through a delta‑consistency objective. The approach outperforms supervised probes and LLM‑as‑judge baselines, achieving up to 71.1% accuracy on sentence‑level hawkish‑dovish classification and producing meeting‑level scores that correlate strongly with inflation indicators and Treasury yield movements.
By Yixuan Tang, Yi Yang
RuleMem is a rule-based memory framework designed for long-term conversational agents. It generates natural-language Horn clauses from dialogue histories and validates them with a Rule Perplexity Consistency mechanism, enabling active guidance of evidence retrieval and reasoning. In evaluations on LoCoMo and LongMemEval_s*, RuleMem outperformed 14 baselines, achieving the highest accuracy on LoCoMo with a 27.47‑point absolute gain (54.3% relative improvement).
By Xingyuan Zeng, Zuohan Wu, Quanming Yao, Yue Wang, Wei Liu, Libin Zheng, Jiuke Wang, Jian Yin