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 recommen- dation system: End-to-end autonomous model optimization with llm agents.arXiv preprint arXiv:2602.10226
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative 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.
NeuroClaw introduces a three-tier multi-agent framework and NeuroBench benchmark that improve executability and reproducibility scores for neuroimaging tasks when used with multimodal LLMs.
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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NeuroClaw Technical Report
NeuroClaw introduces a three-tier multi-agent framework and NeuroBench benchmark that improve executability and reproducibility scores for neuroimaging tasks when used with multimodal LLMs.
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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.