arXiv (All)
open access
Agent Zero Memory: Provenance-Aware Long-Term Memory for LLM Agents
Large language model (LLM) agents need durable, faithful memory of everything a user or organization has said and stored, yet most memory systems commit to a single organizing structure (a fact store, a vector index, or a knowledge graph) and inherit its blind spots. We present Agent Zero Memory, a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Agent Zero Memory: Provenance-Aware Long-Term Memory for LLM Agents#796833
- MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution#927262
- Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents#1063545
- TRACE: Governing Memory Validity in Evolving Multi-Agent Systems#1118229
- The Memory Trust Gap: Capability-Dependent Failures in Persistent-Memory Agents#822274
Record · ID 798246
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive