Machine Learning

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design

Alberto Pepe, Chien-Yu Lin, Despoina Magka, Bilge Acun, Yannan Nellie Wu, Anton Protopopov, Carole-Jean Wu, Yoram Bachrach

Featured May 20, 2026

Original Paper

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Simply

Imagine AI building its own brain! Agents explore different building blocks like Attention and Mamba to design new, super-smart AI models that learn faster and perform better than human-made ones.

In depth

This research introduces AI agents capable of autonomously designing novel neural network architectures and optimizing training processes, a step towards recursive self-improvement. By exploring vast combinatorial spaces of computational primitives like Attention, MLP, and Mamba, these agents, through AIRA-Compose and AIRA-Design, discover hybrid models that outperform human-designed baselines in terms of performance and scaling efficiency.

Key Takeaways

1
AI agents can autonomously discover novel neural architectures (AIRAformers, AIRAhybrids) that achieve superior performance and scaling properties compared to established models.
2
The AIRA-Design framework enables agents to create new attention mechanisms and optimize training scripts, reaching near state-of-the-art results on challenging benchmarks.
3
This work demonstrates a significant advancement in agentic scientific discovery, paving the way for AI systems that can recursively improve their own capabilities.

Conceptual Flow

HIGH LEVEL

Methodology: Agentic Architecture Design

AI agents act like architects, picking and arranging different building blocks to create new AI brain designs.

AI Agent

Building Blocks (Attention, MLP, Mamba)

Designs New AI Brains

Novel AI Architectures

Results: Superior AI Models

The AI-designed brains are smarter, learning more efficiently and performing better on tasks than older designs.

AI-Designed Architectures

Outperforms Baselines

Better Performance

Faster Learning