AIRA_2: Overcoming Bottlenecks in AI Research Agents

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• AIRA2 achieves a mean Percentile Rank of 71.8% at 24 hours on MLE-bench-30, surpassing the previous best of 69.9%.result 95%
• AIRA2's performance steadily improves to 76.0% at 72 hours on MLE-bench-30.result 95%
• AIRA2 addresses the bottleneck of synchronous single-GPU execution with an asynchronous multi-GPU worker pool.method 90%
• AIRA2 addresses the generalization gap caused by validation-based selection with a Hidden Consistent Evaluation (HCE) protocol.method 90%
• AIRA2 addresses the limited capability of fixed, single-turn LLM operators with ReAct agents that dynamically scope their actions and debug interactively.method 90%
• Ablation studies indicate that each component of AIRA2 (asynchronous multi-GPU, HCE, ReAct agents) is necessary for its performance.result 90%
• The 'overfitting' reported in prior work was driven by evaluation noise rather than true data memorization.result 90%
• AIRA2 with 8 GPUs shows a performance advantage of up to 7.5 Percentile Rank points over a single GPU setup at 144 GPU-hours.result 90%

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