AIRA_2: Overcoming Bottlenecks in AI Research Agents
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 AIRA$_2$, 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 sig...
| Authors | Karen Hambardzumyan, Nicolas Baldwin, Edan Toledo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 March 2026 |
| Last updated at source | 13 April 2026 |
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