Tag
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.