An LLM-based evolutionary search discovers novel kernels for high-dimensional Bayesian optimization, achieving an average rank of 1.2 out of 17 on five benchmarks via two-stage proposal and LOO-CRPS selection.
Self-evolverec: Self-evolving recommender systems with llm-based directional feedback.arXiv preprint arXiv:2602.12612
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SAGER equips LLM recommendation agents with per-user evolving policy skills via two-representation architecture, contrastive CoT diagnosis, and skill-augmented listwise reasoning, yielding SOTA gains orthogonal to memory accumulation.
AgenticRecTune deploys five LLM agents (Actor, Critic, Insight, Skill, Online) and a self-evolving Skillhub to handle end-to-end configuration optimization for multi-stage recommendation systems.
citing papers explorer
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Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
An LLM-based evolutionary search discovers novel kernels for high-dimensional Bayesian optimization, achieving an average rank of 1.2 out of 17 on five benchmarks via two-stage proposal and LOO-CRPS selection.
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SAGER: Self-Evolving User Policy Skills for Recommendation Agent
SAGER equips LLM recommendation agents with per-user evolving policy skills via two-representation architecture, contrastive CoT diagnosis, and skill-augmented listwise reasoning, yielding SOTA gains orthogonal to memory accumulation.
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AgenticRecTune: Multi-Agent with Self-Evolving Skillhub for Recommendation System Optimization
AgenticRecTune deploys five LLM agents (Actor, Critic, Insight, Skill, Online) and a self-evolving Skillhub to handle end-to-end configuration optimization for multi-stage recommendation systems.