4arXiv cs.AI (Artificial Intelligence)·
MemLens: Value-aware memory management system with Shapley-style evaluation for LLM agents
MemLens is a proposed memory management system for LLM-based agents that treats memory records as first-class data objects with explicit value scoring, contrasting with existing systems that handle all interaction records uniformly. The system uses Shapley-style evaluation to assess memory utility and provides an interactive analytics dashboard exposing the full memory lifecycle including storage, retrieval, and response generation. A study-copilot application demonstrates the system's ability to compare memory strategies across response quality, retrieval latency, and token consumption metrics.
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MemCon: Learned adaptive memory management for LLM agents via MDP-based policy
Researchers introduce Memory as a Controlled Process (MemCon), a framework that models LLM agent memory operations as a Markov Decision Process and learns an online policy for adaptive retrieval, plan injection, and memory consolidation. The system is backend-agnostic, wraps existing memory implementations, and uses a lightweight tabular contextual bandit with UCB exploration requiring no pretraining and no additional LLM calls. Evaluated across 6 benchmarks, 3 agent frameworks, and 3 LLM backbones, MemCon outperforms static memory baselines by up to 15.2 points in task success while reducing token consumption by 5–20%.
AutoMem: Automated framework trains LLMs to manage memory as a learnable cognitive skill
AutoMem is a new framework that treats memory management in LLMs as a trainable skill, using two optimization loops: one that iteratively revises memory structure via trajectory review by a strong LLM, and one that distills good memory decisions into direct training signal for the agent model. Evaluated on three long-horizon procedurally generated games (Crafter, MiniHack, NetHack), optimizing memory alone yielded 2x-4x performance improvements, bringing a 32B open-weight model competitive with frontier systems like Claude Opus 4.5 and Gemini 3.1 Pro Thinking. The work draws on cognitive science concepts of metamemory and demonstrates that memory management is an independently learnable, high-leverage capability for long-horizon agentic tasks.
MemOS: Self-Evolving Memory OS for LLM Agents with Hybrid Retrieval and Token Savings
MemOS is an open-source TypeScript project providing a memory operating system layer for LLM and AI agents, featuring ultra-persistent memory, hybrid retrieval, and cross-task skill reuse. The project claims 35.24% token savings through its memory management approach. It has accumulated 9,329 GitHub stars with moderate daily momentum (+67). The system targets agent memory persistence and efficiency as a foundational infrastructure component.
MemOps: Benchmark for lifecycle memory operations in long-horizon LLM conversations
MemOps is a new benchmark that reframes conversational memory evaluation as a sequence of explicit lifecycle operations—remembering, forgetting, updating, reflecting—rather than downstream question-answering accuracy alone. Each memory event is represented with a structured trace specifying trigger, target, scope, state transition, and evidence, embedded into long task-oriented conversations via a controllable generation pipeline. Evaluation across long-context, retrieval-based, parametric, and managed-memory systems reveals that current systems fail in distinct, previously conflated ways: session-level retrieval outperforms turn-level retrieval, and long-context models are notably weak at reconstructing ordered memory-state trajectories. The work shifts long-term memory evaluation from black-box final-answer scoring toward interpretable, operation-level diagnosis.
FluxMem: Connectivity-Evolving Memory Framework for LLM Agents
FluxMem proposes a heterogeneous graph-based memory framework for LLM agents that continuously evolves its topology through three stages: initial connection formation, feedback-driven refinement, and long-term consolidation. Unlike static memory repositories, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills successful trajectories into reusable procedural circuits. The system is guided by a single metric for memory generalizability and evolutionary maturity, achieving state-of-the-art results on LoCoMo, Mind2Web, and GAIA benchmarks.
Mem-π: Adaptive Memory for LLM Agents via On-Demand Generation and Decoupled RL
Mem-π introduces a framework where a dedicated language or vision-language model generates context-specific guidance for LLM agents on demand, rather than retrieving static entries from episodic memory banks. The system is trained with a decision-content decoupled reinforcement learning objective that jointly learns when to generate guidance and what to generate, enabling abstention when generation would not help. Evaluated across web navigation, terminal-based tool use, and text-based embodied interaction benchmarks, Mem-π achieves over 30% relative improvement on web navigation tasks compared to retrieval-based and prior RL-optimized memory baselines.
IMLogic benchmark and RootMem framework target implicit logical memory retrieval for personalized LLMs
Researchers introduce IMLogic, a benchmark for evaluating implicit logical memory retrieval in long-dialogue personalized LLM scenarios, addressing gaps in existing semantic-similarity-based retrieval methods. They also propose RootMem, a plug-and-play framework that distills user histories into structured 'root memories' and uses an LLM-based router to activate logically relevant memories alongside semantic retrieval. Experiments show RootMem outperforms retrieval baselines and improves existing memory agents. The work targets a concrete weakness in current personalized LLM memory systems where logically critical memories lack semantic overlap with queries.
UniMem: Self-routing framework for complementary episodic-to-parametric memory in LLM agents
UniMem is a proposed framework for autonomous memory management in LLM agents that addresses the stability-plasticity dilemma in boundary-agnostic task streams. The system uses learnable routing tokens to coordinate between an episodic retrieval buffer (for novel/sparse tasks) and expandable parametric memory blocks (for recurring patterns), inspired by human memory consolidation. Experiments on long-horizon streaming task sequences show an average 4.0 EM point gain over baselines across three backbone models. The work is relevant to continual learning and agent memory architecture research.