Evolutionary coding agents achieve most benchmark gains through a small subset of edit types and by cycling previously deleted code lines rather than developing new algorithmic structures.
Self-Evolving Recommenda- tion System: End-To-End Autonomous Model Optimization With LLM Agents, February 2026
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
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.
NOVA introduces a level-aware agent harness with architecture gradient and verification cascade to automate recommender architecture evolution while reducing silent failures and human effort.
EvoRec deploys four collaborating LLM agents that co-evolve recommendation models and their optimization methods, reporting up to 5.54% offline gains and 1.85% revenue lift in an online A/B test.
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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What Do Evolutionary Coding Agents Evolve?
Evolutionary coding agents achieve most benchmark gains through a small subset of edit types and by cycling previously deleted code lines rather than developing new algorithmic structures.
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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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NOVA: A Verification-Aware Agent Harness for Architecture Evolution in Industrial Recommender Systems
NOVA introduces a level-aware agent harness with architecture gradient and verification cascade to automate recommender architecture evolution while reducing silent failures and human effort.
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EvoRec: Self Evolving Agentic Recommender Systems
EvoRec deploys four collaborating LLM agents that co-evolve recommendation models and their optimization methods, reporting up to 5.54% offline gains and 1.85% revenue lift in an online A/B test.
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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.