Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation. AIRA-Compose uses 11 agents to explore fundamental computational primitives under a 24-hour budget. Agents evaluate million-parameter candidates, extrapolating top designs to 350M, 1B, and 3B scales. This yields 14 architectures across two families: AIRAformers (Transformer-based) and AIRAhybrids (Transformer-Mamba). Pre-trained at 1B scale, these consistently outperform Llama 3.2 and Composer-found baselines. On d...
| Authors | Alberto Pepe, Chien-Yu Lin, Despoina Magka et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 May 2026 |
| Last updated at source | 7 October 2026 |
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