MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments
Summary
This paper introduces MCP-Cosmos, a framework that integrates generative world models into the Model Context Protocol ecosystem to enhance agent planning and execution through predictive simulation in latent space.
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Paper page - MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments
Source: https://huggingface.co/papers/2605.09131
Abstract
MCP-Cosmos integrates generative World Models into the Model Context Protocol ecosystem to enhance agent planning and execution through predictive simulation in latent space.
TheModel Context Protocol(MCP) has unified the interface betweenLarge Language Models(LLMs) and external tools, yet a fundamental gap remains in how agents conceptualize the environments within which they operate. Current paradigms are bifurcated: Task-level planning often ignores execution-time dynamics, whilereactive execution lacks long-horizon foresight. We present MCP-Cosmos, a framework that infusesgenerative World Models(WM) into the MCP ecosystem to enable predictivetask automation. By unifying three disparate technologies, namely MCP, World Model, and Agent, we demonstrate that a “Bring Your Own World Model” (BYOWM) strategy allows agents to simulate state transitions and refine plans in alatent spacebefore execution. We conducted experiments using two strategies, namelyReActandSPIRALwith 2 planning models and 3 representative world models over 20+ MCP-Bench tasks. We observed improvements in Agent’s environment interaction KPI such astool success rateandtool parameter accuracy. The framework also offers new metrics such asExecution Qualityto generate new insights about the effectiveness of world models compared to baseline.
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