The paper introduces the Control Map, an evaluation framework for test‑time model updates that records the specific updates made before each answer and separately assesses acquisition, retention, locality, and performance in new settings. It demonstrates that update scheduling can dramatically affect accuracy, that learning an edit does not guarantee lasting or contained changes, and that no single editor consistently outperforms others across models and benchmarks. The authors argue that evaluations should report update histories and assess these dimensions separately.
By Zehao Zhao, Shangjian Yin, Rui Liu, Chongyang Bai, Mark Ibrahim, Kavosh Asadi, Yuchen Lu, Hang Cui, Hamed Firooz
CALM is a conflict‑aware lifecycle memory governance framework designed for long‑horizon language agents. It transforms heterogeneous records into evidence‑grounded memory cells, updates their validity and lifecycle states using support, conflict, and dependency signals, and admits query‑appropriate evidence through training‑free or learned gating based on temporal applicability. Experiments on LongMemEval‑S and LoCoMo with Qwen and GPT backbones show that CALM outperforms state‑of‑the‑art baselines, improves evidence selection, suppresses erroneous memories, and preserves superseded records for accurate earlier‑state reasoning.
By Chang Li, Xianhao Ou, Xinyan Ma, Hao Li, Haoyuan Xu, Jiafeng Liang, Xiaohan Wang, Jiajun Chai, Guojun Yin, Ming Liu
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:2606. 17328v1 Announce Type: new Abstract: LLM agents increasingly maintain long-term memory of user facts across sessions.
By Xianxuan Long, Zhikai Chen, Shenglai Zeng, Shouren Wang, Kai Guo, Jiliang Tang
The paper introduces RD-Forget, a training‑free framework that separates what a persistent language agent stores from what it uses at answer time. It keeps a source archive of all observations while a query‑conditioned memory view filters evidence relevant to the current question, using a frozen language‑model curator to group facts into semantic slots and preserve multi‑hop relations. The approach employs rate‑distortion principles to stay within a memory budget and demonstrates improvements across conversational memory, knowledge updating, fact consolidation, long‑context reasoning, and personalization tasks.
By Yuhang Li, Yuchen Li
The paper argues that continual learning for world models must differentiate between knowledge that should never be revised—such as physics and object permanence—and knowledge that should be updated when the environment changes. It critiques existing forgetting metrics and benchmarks for failing to capture this distinction, and proposes a differential retention approach that tracks invariant regression testing and revision latency throughout the adaptation process.
By Nishit Anand, Ramani Duraiswami, Dinesh Manocha
Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question. A superseded fact can mislead a current-state an...
CAMR (Compatibility-Aware Memory Replay) is an adaptive framework for continual learning in large language models that jointly manages supervision of new examples and replay of historical data. It uses compatibility checks and a learned memory gate to decide among three actions: general replay of incoming examples, reinforcing compatible related examples, and skipping supervision for mastered duplicates. Evaluated on Qwen3-8B-Base, CAMR outperforms existing methods on WikiBigEdit factual updates and achieves top scores on multiple TRACE tasks while preserving earlier-task performance across diverse domains.
By Jingpu Yang, Tianhao Li, Fengxian Ji, Yufeng Wang, Shuxin Zheng, Xiaoqing Zhang
arXiv:2608. 15507v1 Announce Type: cross Abstract: A consistent concept of the current time is important for temporal reasoning, yet how language models represent the current time is not well understood.
By Suze van Adrichem, Aditi Bhaskar, Diyi Yang, Christopher Potts, Jing Huang
arXiv:2607. 22625v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge, but suffers from knowledge conflicts: when retrieved information contradicts parametric memory, the shared self-attention pathway produces unpredictable outputs.
By Chengzhang Yu, Chenyang Zheng, Zening Lu, Yingru He, Yutong Huang, Yiming Zhang, Yue Xu, Zhanpeng Jin
arXiv:2607. 11020v1 Announce Type: cross Abstract: Continual learning promises a language model that keeps acquiring knowledge after training, with each new fact written into its weights.
By Charles O'Neill
arXiv:2607. 26455v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood.
By Ruxi Gu, Zhenliang Zhang, Wei Wang