{"slug": "evolvetrade-experience-driven-policy-refinement-for-self-evolving-llm-trading", "title": "EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents", "summary": "A new paper titled \"EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents\" addresses the limitation that large language model trading agents rely on static hand-written tool-use policies fixed before deployment, which restricts their ability to adapt how they gather evidence, invoke tools, and verify signals. The work proposes experience-driven policy refinement to let LLM trading agents evolve their tool-use behavior after deployment.", "body_md": "Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment. This limits their ability to adapt how they gather evidence, invoke tools, verify signal", "url": "https://wpnews.pro/news/evolvetrade-experience-driven-policy-refinement-for-self-evolving-llm-trading", "canonical_source": "https://aiflash.com/news/121136/", "published_at": "2026-09-17 03:30:19+00:00", "updated_at": "2026-09-17 03:53:43.045030+00:00", "lang": "en", "topics": ["ai-agents", "large-language-models", "ai-research", "artificial-intelligence"], "entities": ["EvolveTrade"], "alternates": {"html": "https://wpnews.pro/news/evolvetrade-experience-driven-policy-refinement-for-self-evolving-llm-trading", "markdown": "https://wpnews.pro/news/evolvetrade-experience-driven-policy-refinement-for-self-evolving-llm-trading.md", "text": "https://wpnews.pro/news/evolvetrade-experience-driven-policy-refinement-for-self-evolving-llm-trading.txt", "jsonld": "https://wpnews.pro/news/evolvetrade-experience-driven-policy-refinement-for-self-evolving-llm-trading.jsonld"}}