Large language models that write code have already stormed GitHub, but turning them into full-blown research agents—systems that iterate on entire ML pipelines until they medal on Kaggle—has proved trickier. The latest state-of-the-art, AIDE, could grab a medal on roughly 40 % of MLE-bench tasks. Now Meta AI and UCL push that rate to 47.7 % with AIRA, a rethink that says the secret isn’t a flashier LLM, it’s the operators and search policy you wrap around it.
From one-shot “Draft, Debug, Improve” to a toolbox of surgical edits
AIRA introduces OAIRA, a new operator set that goes beyond AIDE’s three blunt actions. Scoped memory keeps prompts lean, “think tokens” force structured reasoning, and a prompt-adaptive complexity cue decides whether the agent should sketch a quick baseline or engineer a deep ensemble. The result: twice the reasoning tokens per call and far less mode collapse.
Search policies finally get room to shine
When AIDE’s old operators were plugged into greedy, MCTS and evolutionary searches, the fancier algorithms gained zero ground—operator bottlenecks were that severe. Swap in OAIRA and those same policies leapfrog greedy search, proving that exploration muscle only pays off once edits are expressive enough.
The scoreboard (MLE-bench Lite, 22 Kaggle tasks)
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AIDE (o1-preview, greedy): 39.6 % medal rate
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AIRA (greedy + OAIRA): 45.5 %
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AIRA (MCTS + OAIRA): 47.7 %
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AIRA (Evolutionary + OAIRA): 47.3 %
All agents ran under identical 24-hour, single-GPU budgets inside AIRA-dojo, a new sandbox that hands each run a root-privileged H200 container yet isolates filesystem side effects.
Mind the generalization gap
The study also spotlights a pitfall for auto-ML agents: validation scores routinely over-estimate test-set gains, steering greedy searches into dead ends. By examining thousands of runs, the team quantifies that “proxy-test gap” and urges future benchmarks to track it explicitly.
Why it matters
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Agent design ≠ model scale. The leap came without touching the underlying LLM (DeepSeek-R1 or GPT-4o). That’s good news for teams capped by API limits.
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Composable recipe. OAIRA operators, MCTS search and the open-source aira-dojo testbed (GitHub link in the paper) can bolt onto any ReAct-style coding agent.
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Toward autonomous ML ops. AIRA’s 24-hour, single-GPU constraint mirrors real-world hack-day budgets, making the findings immediately useful for startups chasing continuous Kaggle pipelines or internal model tuning bots.
Auto-ML agents are no longer judged solely by the size of their LLM brains; the tools they wield and the ways they explore the search space may count just as much. AIRA’s 8-point jump on MLE-bench suggests that the next frontier in agentic ML will be won with sharper scalpels, not bigger hammers.
Paper link: arXiv 2507.02554 (PDF)
