The paper investigates how sequential knowledge editing can degrade a language model’s ability to discern reliable evidence from unreliable evidence without affecting overall accuracy. Using a conservatively tuned LoRA on Qwen2.5‑7B‑Instruct, the authors show that after 1,000 edits the model’s arbitration score for untouched facts drops by 36%, leading to higher error rates on its most confident decisions, while MMLU accuracy remains unchanged. The study also finds that in some model‑method combinations, sequential edits can reduce MMLU to chance levels even though edit success and locality remain perfect.
By Atul Anand
arXiv:2609.25054v1 Announce Type: new
Abstract: For a long-horizon LLM agent, the memory question is not what was once recorded but what \emph{currently holds}. Most designs answer it only indirectly...
By Bowen Qin, Yao Lu
arXiv:2607. 16019v1 Announce Type: new Abstract: AI systems increasingly retrieve from records that revise themselves: issue threads, encyclopedic histories, policy logs, and long conversations.
By Zhaoyang Jiang, Zhizhong Fu, Zicheng Li, Yunsoo Kim, Jiacong Mi, Xuanqi Peng, Fei Teng, Honghan Wu
arXiv:2610.15710v1 Announce Type: new
Abstract: Long-term memory for assistants needs a gate that decides when a new utterance retires an old stored claim. Benchmark labels for this step mark a pair...
By Xupeng Chen, Chenqian Le
arXiv:2606. 25449v1 Announce Type: cross Abstract: A language model's memory can be worse than having no memory at all.
By Alex Kwon
A language model's memory can be worse than having no memory at all. Give a model a memory that kept a wrong conclusion but dropped the work behind it, and it emits that stale value as a confident answer; give the same model an empty memory and it abstains.
arXiv:2608. 04278v1 Announce Type: cross Abstract: Coding agents increasingly work across sessions, but prose notes can preserve a conclusion without the program state that supported it.
By Hwai-Jung Hsu, Cheng-Jan Chi, Hanna Everett
arXiv:2608. 19564v1 Announce Type: new Abstract: Persistent memory can personalize an LLM agent, but an incorrect durable update can silently distort future behavior.
By Baichuan Li, Junyi Yao, Zihao Zheng
The report audits a long‑term‑memory retrieval chain on 500 LongMemEval‑S development questions, evaluating reader performance under an adapted GPT‑4o rubric. Scores vary widely across reader lanes (93–479), with the strongest lanes scoring 479 and 475, and re‑judging the same pass‑1 answers changes three labels, yielding 478. Fixed‑answer knowledge‑update re‑scoring achieves 70/72 or 69/72 depending on the template, and a different‑family reader scores 474, just 1.0 percentage point below the headline pass. The study also reports that live reader request bodies were not retained, that 18 of 23 gains occurred where baseline packets lacked evidence, and that a negative control rejects a verifier that repairs some wrong drafts but breaks many correct ones. Overall, the findings do not establish a new leaderboard leader or a transferable memory advantage, and the released artifacts support packet inspection and re‑scoring but do not reconstruct the method.
By Christopher J. Chanhnourack
The paper introduces the Distributed‑Evidence Paradox, where long‑running LLM agents compress past interactions into persistent memories that may not be fully supported by the interaction history. It defines three key requirements—evidence scope, compositional validity, and admission reliability—and proposes DerivAudit, a framework that checks whether a memory is truly supported by the available history. Experiments on two memory corpora show that expanding the evidence base can recover support for many memories, yet many remain unsupported, and broader evidence alone does not guarantee reliable admission.
By Hongjun Liu, Chen Zhao
arXiv:2608. 07429v1 Announce Type: new Abstract: Long-term memory enables language agents to reuse past facts, preferences, and task experience.
By Yan Zhou, Yue Ouyang, Kaiyang Zheng, Suncheng Xiang
The paper investigates whether knowledge editing truly erases original facts from language models. Using a linear trace probe, the authors find that after editing a fact in GPT‑2‑XL, the original object remains highly decodable from hidden states across three different editing methods, even when the model behaves correctly on edited prompts. This suggests that editing suppresses rather than removes the original association in representational space.
By Priyansh Srivastava, Romit Chatterjee