long-term-memory

This paper introduces a retrieval-admissibility verification framework for long-term agent memory that classifies memory-query pairs as admissible, inadmissible, or unresolved, showing that recall and answer accuracy alone fail to detect policy or context violations. A post-hoc reanalysis of RHELM and MemOps benchmarks shows improved top-20 anchor recall and major reductions in similarity evaluations.

Cognition AI 宣布为 Devin 推出 Memory 和 Dreaming 功能，让 Devin 能跨会话记住用户的工作偏好与经验教训，并通过每日后台 Dreaming 过程优化记忆索引；同时开源了用于构建 Agent 记忆系统的标准规范。

APDMem introduces a hierarchical long-term memory architecture for LLM assistants that applies agent-controlled progressive disclosure over four memory layers, achieving strong LongMemEval performance while accessing only 8% of conversation data.

MemAdapter提出一个反事实适应框架，通过反事实推理、上下文感知反思和证据驱动推理三个组件，缓解LLM智能体长期记忆引发的谄媚问题，在多个基准上提升记忆可靠性。

LangChain's official "Agents From Scratch" guide offers a hands-on, four-step roadmap for building an email agent with long-term memory and human-machine collaboration, covering LangGraph workflows, LLM-as-Judge testing, human-in-the-loop confirmation, and LangGraph Store memory integration, with notebooks and source code included.

Akshay Pachare explains how AI agent memory works across short-term (semantic, episodic, procedural) and long-term scopes, and introduces Oracle AI Agent Memory, a model- and framework-agnostic Python package built on Oracle AI Database that provides governed threads, summaries, durable memories, and scoped retrieval with significantly lower token usage than flat history.

RealCompanion releases a benchmark of ten real AI-companion relationships (27,218 messages over up to 120 days) with derived profiles, personas, and chat ground truth, and finds that past context is rarely needed, memory-reliance detection fails on real messages, and three agent systems reconstruct personas at a 31-fold cost difference with equal F1.

Mnemon is a memory agent that keeps conversations as raw dated records and divides memory work into System 1 fast judgments by a small decision model (Jev) and System 2 planning by an LLM, achieving state-of-the-art LoCoMo (91.7%) and LongMemEval-S (94.4%) scores at low token cost.

This paper introduces DerivAudit, a framework for auditing whether memories stored by long-term LLM agents are actually supported by their interaction history, identifying a 'distributed-evidence paradox' where valid memories often require combining scattered evidence, making unsupported compositions harder to detect.

Introduces Hillock, an open-source engine that uses a neuro-symbolic approach for long-term memory in local agents, improving efficiency with techniques like fact extraction and hyperdimensional gating.

The paper proposes HasMem, an adaptive memory compression method for long-term LLM agents that adjusts memory widths while maintaining frozen LLM parameters, demonstrating improved performance on reconstruction and QA benchmarks.

A review of the 'Today' Personal AI, which offers long-term memory and proactive assistance, integrated with WeChat for fitness training in the Chinese region, with a free basic tier and token bonuses.

The author discusses the challenge of verifying the correctness of AI agents' long-term memory and asks for community experiences on tracking and correcting memory errors.

Proposes VoxPolyMem, an interaction-aware multimodal memory framework with adaptive agentic retrieval for multi-party spoken conversations, achieving state-of-the-art performance on new benchmarks.

EnSIMem is an entity-structured memory architecture for long-term AI agents that organizes interactions into episodes with entity-property indexing, achieving high recall accuracy on benchmarks.

AutoViewMem is a data-driven framework that organizes long-term conversational memory into self-configuring semantic views to improve retrieval and personalization for LLM agents, showing superior performance on benchmarks.

A curated list dedicated to the long-term memory of large language models, covering the latest papers, methods, and evaluation benchmarks.

The paper proposes JustMem, a system that adapts memory access for long-term conversations by balancing discovery breadth and reading fidelity, achieving higher accuracy with fewer tokens.

This paper introduces ReaLMem, the first benchmark for evaluating long-term multimodal memory in AI using authentic personal archives, and proposes ChronoProfiler for temporal weighting to improve personalization in AI companions.

The paper proposes modeling memory as a user-specific dynamical state space with non-uniform geometry to enhance long-horizon personalization in AI, enabling trajectory-conditioned reconstruction for better memory access.