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Jev-Mem:一篇来自UT Dallas的论文提出用TypeSafe AI的Jev结构化输出模型替代LLM来处理智能体记忆系统中的大量小型决策（如记忆分类、关联判断、检索停止条件），仅在最终生成答案时才调用LLM，从而显著降低token开销。
MemFit is a long-term memory system for conversational agents that stores turns verbatim in an append-only store and uses LLM-free insertion and multi-path retrieval to drastically cut memory construction cost and latency while achieving state-of-the-art results on LoCoMo, MemGallery, and LongMemEval-S.
Jev-Mem introduces an agentic memory architecture inspired by System-One/System-Two cognition, enhancing efficiency and effectiveness for long-horizon AI agents with improved scores and faster operations.
The author reflects on challenges in multi-user agentic memory, emphasizing the need for architectural boundaries between private and shared memory sessions, and discusses building an open-source project to address this.
This paper introduces a pre-retrieval failure mode in agentic memory called structurally indirect prerequisite eviction, defines it operationally, provides a deterministic benchmark, and evaluates Dependency-aware Semantic Garbage Collection (DSGC) which improves full-chain retention significantly.
This paper benchmarks the serving cost of three agentic memory systems (Mem0, Hindsight, Mastra Observational Memory) against reference strategies across conversational backbones, finding that cost is driven by internal memory behavior, break-even points vary widely, and no system wins on both cost and accuracy.
IBM Research compares its ALTK-Evolve method to ACE, both agentic-memory systems that let LLM agents learn reusable lessons from past trajectories. ALTK-Evolve delivers individually retrievable guidelines instead of a single evolving playbook, reducing token usage while preserving non-compressed lessons with support counts.
ViSAGE is a multimodal agentic memory framework for long-form video understanding that builds self-correcting, entity-centric memories via cross-modal binding, bidirectional memory refinement, and multi-agent cross-verification, achieving 5.9% higher accuracy than baselines.
A-MEM is an open-source agentic memory system for LLM agents that dynamically organizes memories using Zettelkasten principles, indexes them with ChromaDB, and supports OpenAI and Ollama backends.
EvoEmbedding is a dynamic embedding model that maintains a continuously updated latent memory to generate adaptive representations for long-context retrieval, outperforming larger specialist models and improving agentic workflows.
Proposes a cognitively grounded multi-factor value function for agentic memory in LLM agents, learning interpretable weights to decide what to encode, forget, and retrieve under memory constraints. Improves gold-evidence retention significantly over similarity-only or recency-based baselines.
Researchers from University of Toronto and Vector Institute propose Segment Tree Memory (SegTreeMem), a memory architecture for long-horizon conversational agents that preserves temporal order using a hierarchical segment tree structure for both online construction and retrieval. Experiments across three datasets show nearly 20% improvement in LLM-judge accuracy over non-temporal tree baselines.
This post questions whether combining BM25 and vector search with RRF improves hit rates in agentic memory retrieval, suggesting BM25 alone may suffice.
MemPro is a system-level evolution framework that treats the memory construction–retrieval pipeline as an evolvable program, using an Evolving Agent to iteratively diagnose failures and create improved versions. Experiments on long-horizon benchmarks show consistent improvement over static and prompt-level baselines with favorable performance–cost trade-off.
MemGym is a benchmark for evaluating memory formation in LLM agents over long-horizon tasks, unifying existing agent gyms and synthetic pipelines with memory-isolated scores. It spans tool-use dialogue, multi-turn search, coding, and computer use, and includes a lightweight reward model (MemRM) for efficient evaluation.
This paper shows that continuously consolidating past experiences into textual memory using LLMs degrades memory utility over time, and that preserving raw episodic trajectories outperforms forced consolidation, with implications for robust agentic memory systems.
This paper introduces MemoRepair, a barrier-first cascade repair contract for agentic memory that addresses the problem of stale derived artifacts when source data changes. Experiments demonstrate that MemoRepair significantly reduces invalidated memory exposure and repair costs compared to exhaustive repair methods.