NEW paper from Meta.

(bookmark it)

It's an agent system that autonomously discovers neural architectures that beat Llama 3.2 at 350M, 1B, and 3B scales, all under a 24-hour compute budget.

They get this work by splitting the search into two agents:

AIRA-Compose searches the macro architecture.

AIRA-Design implements the low-level mechanisms.

For devs:

If one agent in your stack is doing both strategy and implementation, split it. Run a planner that picks the structure and an implementer that fills in the mechanisms.

AIRA shows this beats a single end-to-end agent on a real, non-toy search problem. The same split is useful for pipeline assembly, query planning, prompt scaffolding, and tool-use programs.

Paper: https://arxiv.org/abs/2605.15871

Learn to build effective AI agents in our academy: https://academy.dair.ai/