[Paper] LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

Source: arXiv - 2606.10294v1

Overview

Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision. Existing neural architecture search (NAS) methods are typically tailored to a single hardware family, limiting cross-platform comparison and generalization. We introduce Unconventional Hardware Neural Architecture Search (UH-NAS), a hardware-agnostic, LLM-guided NAS framework that integrates language models as evolutionary operators to co-optimize accuracy and inference energy. By exposing hardware as a swappable backend with per-platform energy models, physical constraints, and non-ideality simulators, UH-NAS enables fair system-level comparisons across various backends without modifying the search algorithm. Tested on optical MZI hardware, UH-NAS discovers more diverse, robust architectures than conventional baselines while outperforming existing LLM-to-NAS approaches. Additional ablations on architecture robustness under non-idealities and the role of system prompts highlight the importance of architecture-hardware co-design for emerging computing platforms.

Key Contributions

This paper presents research in the following areas:

• cs.LG
• cs.AI
• cs.AR
• cs.NE
• physics.comp-ph

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.LG.

Authors

• Tyler King
• Timothee Leleu

Paper Information

• arXiv ID: 2606.10294v1
• Categories: cs.LG, cs.AI, cs.AR, cs.NE, physics.comp-ph
• Published: June 9, 2026
• PDF: Download PDF