Agentic Neural Architecture Search
📰 ArXiv cs.AI
Learn how Agentic Neural Architecture Search combines LLMs and NAS to efficiently search for optimal neural architectures, and how to apply this mechanism in practice
Action Steps
- Implement a Large Language Model (LLM) to generate initial architectures
- Use Neural Architecture Search (NAS) to refine and optimize the generated architectures
- Configure the LLM and NAS to divide labor and work together seamlessly
- Test the Agentic Neural Architecture Search mechanism on a variety of tasks and datasets
- Compare the performance of the proposed mechanism with traditional NAS methods
Who Needs to Know This
Researchers and engineers working on neural architecture search and large language models can benefit from this approach, as it enables more efficient and effective search for optimal architectures
Key Insight
💡 Agentic Neural Architecture Search can bridge the gap between LLM-driven design and NAS-driven search, enabling more efficient and effective architecture search
Share This
🤖 Combine LLMs and NAS for efficient neural architecture search! 🚀
Key Takeaways
Learn how Agentic Neural Architecture Search combines LLMs and NAS to efficiently search for optimal neural architectures, and how to apply this mechanism in practice
Full Article
Title: Agentic Neural Architecture Search
Abstract:
arXiv:2607.07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task. Large language models (LLMs) can generate architectures in an open-ended space, but how to optimally divide the labor between LLM-driven design and NAS-driven search remains unexplored. We propose a mechanism that bridges these two p
Abstract:
arXiv:2607.07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task. Large language models (LLMs) can generate architectures in an open-ended space, but how to optimally divide the labor between LLM-driven design and NAS-driven search remains unexplored. We propose a mechanism that bridges these two p
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