arXiv:2602.12612v2 Announce Type: replace-cross Abstract: Traditional methods for automating recommender system design, such as Neural Architecture Search (NAS), are often constrained by a fixed search space defined by human priors, limiting innovation to pre-defined operators. While recent LLM-driven code evolution frameworks shift fixed search space target to open-ended program spaces, they primarily rely on scalar metrics (e.g., NDCG, Hit Ratio) that fail to provide qualitative insights into model failures or directional guidance for improvement. To address this, we propose Self-EvolveRec,
The increasing sophistication of LLMs and the recognition of limitations in traditional recommender system design automation are converging to enable new approaches.
This development indicates a significant leap in AI's ability to self-optimize and evolve complex systems, moving beyond human-defined constraints towards more autonomous agentic behavior.
Recommender systems and potentially other AI-driven development processes can now benefit from qualitative, directional feedback from LLMs, leading to more robust and adaptable systems than those relying solely on scalar metrics.
- · AI developers
- · E-commerce platforms
- · Content recommendation services
- · Deep learning researchers
- · Traditional NAS approaches
- · Manual recommender system optimization
- · Systems focused purely on quantitative metrics
Recommender systems become more efficient and personalized through LLM-driven self-evolution.
The methodology could be generalized to other AI system designs, reducing the need for human intervention in model development and evolution.
This capability could accelerate the development of truly autonomous AI agents capable of self-improvement and complex problem-solving in open-ended domains.
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Read at arXiv cs.AI