long-term-memory

The author argues the agent harness is fundamentally a distributed systems problem and cites DeepSeek's Super-agent writeup, which shows a two-level Router/Worker design beats a single chat session on latency and delegation reliability, while raising long-term memory as the key open problem.

提出了ETAMem多视角记忆框架，通过解耦异构对话信息并构建Event、Affect、Time三个互补的层次化视图，提升长期对话智能体的记忆检索效果，在LoCoMo和LongMemEval-S上分别实现12.46%和7.96%的相对准确率提升。

TrawMem reframes LLM agent memory systems as task-oriented role-adaptive workspaces, organizing interaction histories into threads and selecting complementary memories by their task roles, achieving consistent macro-F1 gains of 2.23–3.14 points over representative memory systems on LoCoMo and MemGallery benchmarks.

论文提出ConMem算法，将LLM智能体的长期记忆保留决策重构为反事实因果推断，并构建MemAlign基准（1,079个记忆案例）来研究记忆错位问题，实验表明该方法在缓解过度保留偏差的同时保持个性化效用。

SLICE-Mem 提出一种轻量级 LLM Agent 长期记忆框架，通过显著性引导的记忆准入与基于谓词的语义抽取实现免 LLM 调用的在线记忆构建，在保持接近 MemOS 基线精度的同时将 token 消耗降低 92.4%–99.5%。

A developer shares a tutorial for giving AI agents persistent memory across sessions using Pydantic AI and a vector database: extracting facts from conversations, embedding them, and retrieving relevant memories to inject into a new session's context.

The paper introduces MOSAIC, an omni-modal benchmark of 923 questions over persona-grounded multi-session histories spanning text, image, video, and audio, designed to diagnose where conversational agents lose evidence across write, retrieve, deliver, and read stages rather than just producing a single leaderboard score.

M-Plan is a closed-loop multi-agent system framework that integrates long-term and short-term memory with global-local and past-current guidance to improve LLM agents' planning on long-horizon multi-turn tasks, outperforming baselines on ALFWorld, Webshop, and PDDL.

A new Meta paper introduces ProPerMem, a year-long egocentric video memory benchmark (~618K tokens) showing that proactive AI assistants fail primarily at deciding when to speak, not what to say—frontier models like GPT-5.3-Chat score 96% with oracle evidence but drop to 15-38% when choosing their own timing.

HABIT-Bench is a new benchmark evaluating whether LLM agents can induce user habits from longitudinal, weak evidence in memory across food, finance, software, and travel domains. Eight memory methods evaluated with Qwen3-235B-A22B and GLM-5.3-Flash reach only 33.61% macro accuracy on four-choice tasks, showing that recalling history does not equal knowing what should guide decisions.

Kairos 项目发布了一个实验：让三个 LLM 法官对比 AI 智能体真实的 30 天生活记忆与伪造记忆，结果几乎无法区分（分差仅 +0.032），说明仅凭文本无法判断记忆是否真实发生过，只有日志才能证明。

该论文提出了 QEMem 框架，将 LLM 检索的记忆重组为查询特定的证据集合，以弥合“相关性-充分性差距”，在 LoCoMo 基准上以 Claude Opus 4.5 达到 91.43% 的 LLM-judge 准确率，超越 MemR3 和 NEMORI 基线。

腾讯混元发布 Hy-Memory-30B-A3B，一个约 3B 激活参数的紧凑型长期记忆写入模型，可替换 Mem0、MemOS 和 Hy-Memory 等外部记忆框架中的通用 LLM，采用记忆在线蒸馏（OPD）与归因记忆强化学习两阶段训练，在四个记忆框架的交叉评测中平均得分超越 Claude Opus 5。

A company building memory infrastructure introduces 'glasshouse', a draft long-term memory benchmark with 2,847 questions over a 1.97M token conversation in 10 languages, reporting 17 separate axes (including fact-update consistency and conflict resolution) without a single headline score, and asks the agent-memory community for feedback.

这篇论文提出 MaLoW（Memory as LoRA Weights），将流式视频的长期历史增量编码进固定大小的 LoRA 权重中，冻结主干模型并把当前观测保留在输入里，从而分离长期记忆与实时感知。该方法在 OVO-Bench 和 StreamingBench 上取得领先表现，并在机器人任务上也提升了成功率。

腾讯开源了 Agent Memory，一个可 100% 本地基于 SQLite 运行的 AI 智能体长期记忆层，无需云端 API 或向量数据库，报告可减少约 61% token，PersonaMem 准确率从 48% 提升到 76%，且记忆可读可追溯。

The paper proposes T-Mem, a temporal-structure-based memory framework for LLM agents in long-horizon multi-party conversations, using Temporal Evidence Discovery and Temporal Memory Structuring to recover locally concentrated evidence and reconstruct event progressions as an evidence forest, outperforming existing memory frameworks on two benchmarks.

Oh-My-Hermes 3.0.2 发布，新增 SkillEx 技能约束与 FenceGuard 沙箱安全机制，为 Hermes Agent 提供技能路由、长期记忆与跨 GPT、Claude、DeepSeek、Kimi、GLM 的并行沙箱编码代理能力。

三星研究院提出 Gated Memory 框架，在事实抽取前引入准入门控与条件增强阶段，以保留原始话语中的关键上下文信号，提升对话式 AI 的长期记忆质量，在 LoCoMo-10 基准上 LLM-judge 准确率相对提升 2.6%。

A defense-in-depth evaluation framework that jointly studies internal activation steering and external memory-gate defenses against memory-induced sycophancy in LLMs, finding that selective Router Gate memory filtering preserves accuracy better than full memory removal, while inverse activation steering offers no statistically significant additional benefit.