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The paper presents MFM, a multimodal filesystem memory architecture that combines episodic, spatial, semantic, procedural, and working memory to help long-horizon embodied agents reuse experience, achieving improved performance on Minecraft and computer-use benchmarks across three base models.
MemScope 是一个由中国科学院提出的框架，通过工具使用的 Patch Agent 检查原始执行轨迹、运行时内存状态和现有架构代码，实现对智能体记忆架构的证据驱动、局部化原位进化，在 GAIA、xBench 和 ALFWorld 上均取得显著提升。
A commentary exploring how much of today's agent memory stack — retrieval, summarization, context compression — is merely a workaround for stateless model architectures, and how recurrent-memory designs like BDH-CQ could shift that responsibility into the model itself.
Percepta unveils Spotlight, a new LLM architecture that separates a fixed-size intelligence module from an unbounded, writable memory, letting the model accumulate knowledge and skills without changing its weights while keeping per-token access cost constant regardless of memory size.
This paper explores an alternative non-Transformer language model architecture that separates memory, deterministic execution, and contextual computation through explicit interfaces, using an attention-free Multi-Mesh model, reconstructive document memories, and a modular core with typed residual assembly. The authors frame it as a research prototype with released checkpoints, inviting independent testing and discussion rather than claiming superiority over Transformers.
OpenClaw Enterprise launched a new cryptographic agentic memory framework called STRATA, which replaces mutable storage with an append-only signed log governed by FSRS6 decay, paired with Opator Lite, a sub-millisecond deterministic execution gate designed to neutralize memory poisoning attacks like MemGhost and CVE-2026-44112.
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.
LOCI introduces a hybrid spatial memory architecture for streaming video world models, combining key-value caches with a recurrent linear-attention memory conditioned on camera geometry. It reproduces revisited content more faithfully than full-softmax baselines while reducing peak memory by about 30% and enabling constant-memory streaming of long videos.
The article presents Jev-Mem, a new agentic memory architecture that uses a System-One controller for fast memory operations and System-Two for reasoning, achieving 6.6x faster memory construction and 36.7% lower query latency while improving accuracy by 11% on the LoCoMo benchmark.
An AI enthusiast shares a human-in-the-loop setup where an AI companion named Cal interacts with a D&D campaign transcript, providing an interactive co-listening experience during long drives.
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.
A developer shares a prototype memory architecture for multi-agent systems with separate storage for episodic, semantic, and procedural memories and intent-based retrieval, seeking feedback on scalability and design choices.
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.
VoiceMem introduces a dual-brain streaming memory architecture for speech language models that improves retrieval accuracy, emotional personalization, and real-time efficiency.
ECHO is a cognitively inspired, auditable memory architecture for long-horizon agents, evaluated on benchmarks like LoCoMo and LongMemEval-S with high retrieval performance.
A computer science paper formalizes filesystem-based memory as long-term storage for autonomous AI agents, showing that organizing memory as hierarchical markdown files can roughly halve retrieval costs compared to vector stores.
FocusMem introduces a latent memory interface for GUI agents that separates content retention, state-conditioned readout, and a trust gate to improve memory reliability. It consistently outperforms fixed-memory baselines across five GUI-agent benchmarks.
This paper introduces shared selective persistent memory for agentic LLM systems, which retains reusable context like task specs and data schemas while discarding session-specific reasoning traces, achieving 96% task completion and significant token cost reductions in enterprise scenarios.
Maka is a local-first desktop AI workstation. Recently, the number of active contributors has increased. The team is optimizing UI, refactoring architecture, designing Memory and Agent FS interfaces, and adding multiple Agent capabilities, aiming to provide observable, controllable, and sustainably recoverable Agent experiences.
Explains why production AI agents require a three-tier persistent memory stack—session logs (Zep), personalization (Mem0), and governed context (ContextNest)—to avoid retrieval of stale or conflicting facts.