Hermes, an LLM, breaks down complex network tasks into manageable chunks for LLMs
Blueprint-based approach helps LLMs understand and model network behavior
Hermes introduces a chain-of-agents framework that helps networks become autonomous by creating digital twins through blueprints. It addresses the challenge of manual network modeling by enabling LLMs to understand network behavior and generate accurate models for diverse scenarios.
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https://arxiv.org/abs/2411.06490
🌍 Original Problem:
→ Current networks heavily rely on human experts for modeling and policy-making, making it costly and unsustainable for large-scale operations.
→ LLMs struggle with network modeling tasks due to computation errors, limited domain knowledge, and hallucinations.
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🔧 Solution in this Paper:
→ Hermes uses a chain of LLM agents working in three phases: Design, Coding, and Feedback.
→ The Designer agent creates blueprints - step-by-step logical blocks for building network digital twins.
→ The Coder agent transforms blueprints into executable Python code.
→ A feedback loop validates and refines the blueprints through numerical evaluation.
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💡 Key Insights:
→ LLMs need structured frameworks to handle complex network modeling tasks
→ Breaking down tasks into design and coding phases improves reliability
→ Continuous feedback and validation mechanisms reduce hallucinations
→ Blueprint-based approach enables reusable network modeling solutions
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📊 Results:
→ Hermes achieved 85% success rate on simple tasks, outperforming chain-of-thought (35%)
→ Maintained 75% success on complex tasks while others dropped to 5-25%
→ Demonstrated consistent performance across varying network configurations
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