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Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer
A position paper proposes a 'Foundation Model Operating System' (FMOS) as a new system layer to unify fragmented AI agent frameworks by virtualizing foundation model interactions, enabling portable behavior and adaptive governance.
Sep 18, 2026
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
EvolveTrade is a research framework that enables LLM-based trading agents to iteratively refine their tool-use policies using real-world trading feedback, improving risk-adjusted returns without modifying the underlying LLM.
Sep 17, 2026
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
WMLLM is a new self-evolving optimization agent framework that uses large language models for world modeling to improve sample efficiency in black-box optimization, especially for multi-objective molecular design.
Sep 3, 2026
From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents
Researchers propose EvoSOP, a framework enabling LLM agents to automatically synthesize atomic tool actions into reusable Standard Operating Procedures (SOPs), improving task success rates and reducing interaction rounds in experimental settings.
Jul 10, 2026