arXiv (All)
open access
Causal Episodic Memory for Feedback-Driven Agent Repair
LLM agents that repair failures often discard successful corrections, forcing later episodes to rediscover similar solutions. We study whether finalized repair outcomes can improve subsequent Text-to-SQL episodes without parameter updates. We introduce MERIT, a training-free agent that maintains an
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
- What Makes Agent Memory Useful for Reliable Unanswerable Question Handling?#781202
- What Makes Agent Memory Useful for Reliable Unanswerable Question Handling?#779827
- Learning principle for the adaptive shaping of epigenetic memory repertoires#248592
- Reinforcement drives within- not between-trial motor adaptation#5538
- Improving Generalization and Robustness in Offline Reinforcement Learning via Boundary-Aware Data Augmentation#1034068
Record · ID 998321
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