The paper introduces two inference‑time designs—factor‑compiled admission (FC) and permission‑semantic admission (PS)—to govern persistent memory in large language model agents without retraining. FC evaluates entire memory entries, while PS decomposes entries into typed units, both using deterministic policies to decide what recalled information enters the working context and how it is expressed. Experiments on a development suite and an external benchmark show that FC and PS reduce failure rates and cross‑domain leakage compared to verbatim injection, though they increase personalization failures and do not meet all preregistered criteria.
By Chang Liu, Deliang Ding
arXiv:2606.28876v4 Announce Type: replace-cross
Abstract: Memory-Mediated Learning Architecture (MMLA) separates slow base parameters theta, a bounded numerical policy carrier Phi, and a bounded auth...
By Junyi Zou, Avrova Donz
The paper introduces Causal Memory Policy (CMP), a framework that identifies the utility of memories in memory‑augmented language models by intervening on retrieval rather than on storage. CMP reserves fixed context slots for memories sampled with known propensities and estimates utility using self‑normalized inverse propensity weighting, providing unbiased estimates and exact variance. Experiments show that CMP improves discrimination between required and non‑required memories and reveals that identified utility alone is insufficient for retention decisions across unseen queries.
By Arman Behnam, Binghui Wang
arXiv:2609.25853v1 Announce Type: new
Abstract: Learned-memory methods store information in an explicit table and consume it through a separate reader, allowing addressing, storage, and reading to be...
By Mingyuan Li, Guangsheng Yu, Juyuan Zhang, Xu Wang, Zhibo Man, Haonan Zhang, Shaoxiong Ji
The paper presents a retrieval‑admissibility verification framework for long‑term memory agents, classifying each memory‑query pair as admissible, inadmissible, or unresolved. It evaluates the framework on public benchmarks (RHELM and MemOps), showing improved anchor recall and reduced exact similarity errors, while also revealing that existing verifiers miss certain inadmissible exposures. The study highlights the need for separate checks on candidate support, admissibility, prompt exposure, and answer disclosure to ensure safe memory retrieval.
By Zi Wang, Xingqiao Wang, Emmanuel Addai, Devika Ambekar, Xiaowei Xu
arXiv:2606. 26806v1 Announce Type: new Abstract: Long-running language agents need more than memory access.
By Haoliang Han
The paper investigates how to inject knowledge into addressed memory modules of language models. Using controlled experiments on GPT‑2 and production‑memory tests on Qwen3.8‑Flash‑Next‑FP8 and DeepSeek‑V4.1‑Flash, the authors compare row‑only and row‑plus‑projection updates, with and without fixed module‑residual anchoring. Results show that while anchoring reduces drift, lexical hits do not guarantee entity‑specific retrieval, and first‑token rankings alone do not ensure full answer success, indicating that writable memory is feasible but limited in scope.
By Wei Gao (Shibei Qiyuan AI Technology), Cheng Tian (Shibei Qiyuan AI Technology), Duan Yang (Xi'an Jiaotong University), Jianqing Yin (Shibei Qiyuan AI Technology)
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
arXiv:2607. 20972v1 Announce Type: new Abstract: Coding agents ship with one kind of memory: documents.
By Swapnanil Saha
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 D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides a structured framework that uses paired evidence, protected-slice monitoring, and trace-level localizability to decide whether to promote, flag, or reject memory interventions. The authors also present DCR, a metric for measuring failure localizability, and D$^2$ACCI‑Eval, a reusable artifact for gate replay, demonstrating significant performance gains on three public benchmarks and highlighting the importance of traceable, statistically grounded diagnostics.
By Xule Liu, Yijun Liu, Chao Li, Shao Kun
Addressed conditional memory provides a local surface for post-hoc knowledge injection, but a successful write need not transfer to a new question or preserve untargeted behavior. This study combines...