arXiv (All)
open access
AutoRSE: Evaluating LLM Agents as Recommendation Engineers for Self-Evolving Recommender Systems
Recommender Systems Engineer Agents (RSE-Agents), which use LLM-based agents to autonomously improve recommendation algorithms, are emerging as a promising paradigm. However, their capabilities and limitations remain poorly understood due to the lack of a unified, standardized evaluation framework 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
- Toollery: Scaling LLM Agents to Thousands of Skills and Tools#1036901
- PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems#1324590
- Learning Reusable Agent Subroutines from Experience#1344605
- The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations#819452
- The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations#809456
Record · ID 1468832
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