Tag
MemFuse introduces MemFuseBench, a benchmark for evaluating multi-source memory fusion in AI agents, and proposes MemFuse, a structured memory system that integrates fragmented observations while maintaining source traceability.
This paper introduces EpicStar, a framework that uses episodic memory and dynamic retrieval to help LLM agents maintain strategic coherence in long-horizon tasks like StarCraft II, achieving higher win rates with significantly fewer tokens.
This paper presents a deterministic, zero-model pipeline that compiles passively captured screen activity into structured 'activity frames' for agent memory, reducing a day of raw capture to a prompt-ready context block 86× smaller and achieving 98.4% QA accuracy. It also introduces measurements of routine overhead ratio and recurrence to model agent costs.
This paper introduces ScrubJay-MEM, an LLM agent memory system inspired by scrub jay episodic memory, which uses type-conditioned temporal decay to manage memory perishability. It also proposes the Temporal Generalization Test (TGT) benchmark and shows improved performance over existing memory systems on temporal reasoning tasks.
UniMem proposes a self-routing framework that combines episodic and parametric memory for LLM agents, enabling adaptive memory management in boundary-agnostic task streams without task labels.
This paper investigates whether long-context language models exhibit episodic-like order memory similar to humans, using a temporal order memory task based on a novel. The authors find that models show the same distance effect and uncover a single attention head reinstating temporal context, supporting temporal context reinstatement as a mechanism for episodic-like memory in LLMs.
A Twitter thread offering strategic advice on building self-learning agents that improve with use, covering dual learning sources, memory types, and data ownership.
A developer shares their experience building an AI agent with memory using the Anthropic SDK and TypeScript, explaining the differences between working, episodic, semantic, and procedural memory and the challenges of scaling memory for production.
An ex-Google engineer explains AI agent memory architecture in 12 minutes, covering working memory and three memory layers (procedural, semantic, episodic) with a summarizer to prevent token bloat, as used by Claude.
WISE proposes a long-horizon agent framework for Minecraft that enhances low-level controllers with a Causal Event Graph for episodic memory, enabling robust recall under viewpoint changes and opportunistic task reordering via causal reasoning. It also features a multi-scale progressive exploration strategy and demonstrates improved success and efficiency on long-horizon sparse tasks.
S3Mem proposes a structured spatiotemporal scene-event memory framework for long-horizon interactive question answering, using anchor-sensitive retrieval and token-budget-aware evidence interface to outperform standard RAG in multiple environments.
A novel memory retrieval system inspired by episodic memory theory achieves state-of-the-art 96.4% top-50 accuracy on the LongMemEval benchmark using Gemini Flash, outperforming larger Pro-based baselines by isolating retrieval quality from model capability.
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.
A study finds that continuously updating consolidated memories in LLM-based agentic systems degrades performance, and that retaining raw episodic trajectories is more reliable. Experiments on ARC-AGI show that even GPT-5.4 fails more often after consolidation.
The paper introduces MemQ, a method that integrates Q-learning into self-evolving memory agents by using eligibility traces over provenance DAGs to solve credit assignment problems in episodic memory retrieval.
This research demonstrates that continuously updating LLM agent memories through distillation and consolidation loops causes performance regression, even when trained on ground-truth solutions. The study finds that episodic-only retention outperforms text-based consolidation, highlighting significant flaws in current self-improvement paradigms.
This paper introduces CASCADE, a framework for deployment-time learning that allows Large Language Models to adapt continuously through episodic memory and contextual bandit optimization without modifying model parameters.