Agents

// Agent Zero Memory // This work separates three things that agent memory systems usually collapse into one. If you build agents with long-…

// Agent Zero Memory // This work separates three things that agent memory systems usually collapse into one. If you build agents with long-term memory, this memory design is a worth a read. Here is h

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// Agent Zero Memory // This work separates three things that agent memory systems usually collapse into one. If you build agents with long-term memory, this memory design is a worth a read. Here is how it works: Agent Zero Memory runs an episodic events timeline, an entity-event knowledge graph, and a curated documentary memory of durable facts side by side over the same history. A retrieval turn passes through an intent gate, then a source router, then three concurrent agentic searches, one per system. Every stored item carries its origin, timestamp and evidence pointer, and answers run under a citation lock, so a reply may cite only evidence its reader actually opened. When the evidence is missing the system abstains. It reaches 95.60% on LongMemEval and 93.60% on LoCoMo, both new highs. The cost result is the more useful one for AI builders. Across eight backbone models accuracy moves by 3.4 points while per-query cost moves about 30x, with near state of the art quality available at up to 20x lower cost per query. Memory design is driving the quality here. Paper: https://arxiv.org/abs/2608.29606 Chat with Paper: https://academy.dair.ai/papers/agent-zero-memory-provenance-aware-long-term-memory-for-llm-agents-2608.29606

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Source: DAIR.AI (X) | 2026-09-02

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