arXiv (All)
open access
Learning What Not to Forget: Long-Horizon Agent Memory from a Few Kilobytes of Learning
Long-running language-model systems accumulate interaction history that outgrows the context window, so they must continually evict. When an eviction policy drops a task-critical detail, for example an access token issued at login or a path the next call needs, the action fails. We present LRE (Lear
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
- Learning What Not to Forget: Long-Horizon Agent Memory from a Few Kilobytes of Learning#920949
- Memory Control Signals Emerge Before Action in Long Horizon Agents#1063497
- Mitigating Memorization In Language Models#1144698
- 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
Record · ID 925184
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