GLOBE: Evolving Structured Agentic Memory for E-commerce Product Optimization in Generative Search

Key points

• However, existing approaches often treat structured listings as free-form text, leaving effective strategies implicit in prompts, search trajectories, or rewritten outputs rather than accumulating them as a reusable agentic memory.1
• To address this limitation, we introduce GLOBE, an agentic framework that drives continual self-improvement by storing optimization knowledge in an evolving handbook of explicit, field-scoped behaviors.1
• Experiments show that structured behaviors outperform monolithic prompt optimization, while feedback-driven evolution and moderation further improve effectiveness and reduce downside risk.1
• The learned behaviors also remain effective on unseen product categories, suggesting that agentic memory evolution supports interpretable and transferable product optimization.1

Sources (1)

• [1]GLOBE: Evolving Structured Agentic Memory for E-commerce Product Optimization in Generative Search
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 04:33 PM

  Optimizing product listings for generative search requires language agents not only to rewrite effectively but also to continually self-improve by retaining successful strategies across diverse categories.

  However, existing approaches often treat structured listings as free-form text, leaving effective strategies implicit in prompts, search trajectories, or rewritten outputs rather than accumulating them as a reusable agentic memory.

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