arXiv (All)
open access
Remember by Asking: Retrieval-Induced Memory Evolution for LLM Agents
Long-term memory is essential for language agents to maintain coherent and effective behavior over extended, multi-session interactions. Existing memory systems mainly use retrieval at read time, while write-time memory formation still relies on direct extraction or compression. However, when future
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
- Evaluating Memory Structure in LLM Agents#998107
- Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents#821767
- Heavy-Tailed Memory Traces in Long-Horizon Language Agents#1154633
- When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents#972506
- Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents#1063545
Record · ID 1128708
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