Dimensions overall score 9.0

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• The paper presents a five-level taxonomy of AI integration for research.method 95%
• The framework can turn CLI coding agents into autonomous research assistants through methodological rules formulated as agent prompts.technical 90%
• The framework runs inside a sandboxed container and works with any frontier LLM through existing CLI agents.technical 90%
• The framework scales from personal-laptop prototyping to multi-node, multi-GPU experimentation across compute clusters.technical 85%
• The longest autonomous session ran for over 20 hours, dispatching independent experiments across multiple nodes without human intervention.result 95%
• The framework is not intended to replace the researcher in the loop, but to augment them.limitation 95%
• The framework is simple enough to install and use within minutes.technical 80%
• The paper includes case studies from deep learning and mathematics demonstrating the framework's application.method 90%
• The framework provides a five-level taxonomy of AI integration for researchers.method 90%
• The open-source framework turns CLI coding agents into autonomous research assistants through methodological rules formulated as agent prompts.method 95%
• The framework runs inside a sandboxed container and is compatible with any frontier LLM through existing CLI agents.technical 95%
• The framework scales from personal-laptop prototyping to multi-node, multi-GPU experimentation across compute clusters.technical 95%

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