arXiv (All)
open access
On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature. However, the reliability aspects of these methods have been critically overlooked. In this work, we conduct 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
- The Metaphysics of Memory#753508
- RLTL;DR: Self-improvement by Internalizing Self-generated Feedback#1141947
- MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks#1034704
- Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads#259508
- Understanding Stage-Wise Utility-Risk Trade-offs in LLM Agent Memory#799399
Record · ID 973315
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