arXiv (All)
open access
Memory as Middleware for Self-Improving AI Agents
AI agents are stateless across sessions by default and therefore operationally amnesic: each session begins with little durable knowledge of prior failures, repairs, preferences, or successful strategies. As a result, agents repeat the same mistakes and discard hard-won experience. The dominant fix
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 Memory: Characterization and System Implications of Stateful Long-Horizon Workloads#1062655
- Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents#1063545
- Supra Cognitive Modes: A Routed Architecture for Agent Memory#386921
- Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents#373485
- Memory Is a Derivation: The Distributed-Evidence Paradox in Long-Term Agents#1130972
Record · ID 1116422
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