RESEARCH

AIRA₂: Overcoming Bottlenecks in AI Research Agents

April 16, 2026

Abstract

Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample throughput, limiting the benefit of search; (2) a generalization gap where validation-based selection causes overfitting and performance to degrade over extended search horizons; and (3) the limited capability of fixed, single-turn LLM operators imposes a ceiling on search performance. We introduce AIRA₂, which addresses these bottlenecks through three architectural choices: an asynchronous multi-GPU worker pool that increases experiment throughput linearly; a Hidden Consistent Evaluation protocol that delivers a reliable evaluation signal; and ReAct agents that dynamically scope their actions and debug interactively. On MLE-bench-30, AIRA₂ achieves a mean Percentile Rank of 81.5% at 24 hours and 83.1% at 72 hours, outperforming the strongest baseline, which achieves 72.7%. On AIRS-Bench, AIRA₂ exceeds human state-of-the-art on 6 out of 20 diverse research tasks. Ablations confirm that each architectural component is necessary, that performance follows a predictable scaling law that transfers across LLM backbones, and that the "overfitting" reported in prior work was driven by evaluation noise rather than true data memorization.

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AUTHORS

Written by

Nicola Cancedda

Pontus Stenetorp

Alexis Audran-Reiss

Alisia Lupidi

Anton Protopopov

Bassel Al Omari

Carole-Jean Wu

Derek Dunfield

Despoina Magka

Edan Toledo

Hela Momand

Ishita Mediratta

Jakob Foerster

Jean-Christophe Gagnon-Audet

Karen Hambardzumyan

Kelvin Niu

Martin Josifoski

Michael Kuchnik

Michael Shvartsman

Nicolas Baldwin

Parth Pathak

Rishi Hazra

Tatiana Shavrina

Thomas Simon Foster

Yoram Bachrach

Publisher

arXiv

Research Topics

Core Machine Learning

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