The paper introduces a framework that enhances senior design projects through multi-agent LLMs, enabling collaborative problem-solving while simulating diverse expert perspectives.
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https://arxiv.org/abs/2501.01205
Solution in this Paper 🛠️:
→ The framework deploys 8 specialized LLM agents, each focusing on specific aspects like problem formulation, system complexity, and ethical considerations.
→ Agents coordinate through a central system using Camel AI, enabling collaborative analysis of student projects.
→ Each agent evaluates projects based on predefined metrics, simulating expert feedback in areas like technical innovation and methodology.
→ The system provides real-time feedback through a web interface, allowing students to ask follow-up questions.
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Key Insights 💡:
→ Multi-agent systems outperform single-agent approaches in evaluating complex projects
→ Specialized agents can effectively simulate diverse expert perspectives
→ Structured evaluation metrics improve feedback consistency
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Results 📊:
→ Multi-agent system achieved 89.3% higher accuracy compared to single-agent approaches
→ Lower Mean Absolute Error (0.205 vs 0.388) in alignment with faculty evaluations
→ Improved performance in technical categories (MAE 0.345 vs 0.855)
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