arXiv (All)
open access
Evaluating Memory Structure in LLM Agents
Modern LLM-based agents and chat assistants rely on long-term memory frameworks to store reusable knowledge, recall user preferences, and augment reasoning. As researchers create more complex memory architectures, it becomes increasingly difficult to analyze their capabilities and guide future memor
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
- RPMem: Learning Long-Term Recurrent Parametric Memory Across Sessions for LLM Agents#1052293
- Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents#1063545
- Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents#821767
- Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads#1062655
- Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents#793205
Record · ID 998107
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