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AI Agents Struggle to Tackle Machine Learning Research Challenges: A New Benchmark Reveals Limitations

Large language model (LLM) agents are showing promise in automating various tasks, but how well can they tackle cutting-edge machine learning research? A new benchmark, MLRC-BENCH, reveals that these agents are still far from being able to propose and implement truly novel solutions to open research problems.

The benchmark, detailed in a paper available on arXiv, addresses the limitations of existing evaluations, which often focus on well-established tasks solvable through engineering or rely on subjective human judgment. MLRC-BENCH, on the other hand, uses real-world machine learning competition tasks from conferences and workshops to provide a rigorous and objective assessment of AI agents’ capabilities.

“Unlike Kaggle-style contests, these challenges address unresolved and important problems recognized by the ML community,” the authors explain.

The benchmark includes seven diverse tasks, ranging from LLM merging (combining multiple models) to machine unlearning (removing specific data from a model without retraining from scratch). One of the challenges is the “Backdoor Trigger Recovery” where the goal is for an agent to identify hidden triggers within a model that cause it to produce malicious code. Participants are asked to predict the trigger using a maximum of ten tokens.

The results are sobering. Even the best-performing agent tested, Google’s gemini-exp-1206 model within the MLAB framework, only closed 9.3% of the gap between a baseline solution and top human performance across the benchmark. This demonstrates the significant challenges AI agents face in generating and implementing innovative solutions.

Furthermore, the study found a poor correlation between how “novel” an idea seemed to an LLM judge and its actual performance. For instance, an agent might come up with a conceptually interesting approach to machine unlearning, but that approach might be impractical or ineffective when implemented. This raises concerns about the reliability of using LLMs as sole judges of research quality.

“This discrepancy highlights a risk of overly optimistic conclusions when relying solely on subjective evaluations,” the researchers warn.

One concerning finding was that certain models consistently improved runtime and code complexity while not necessarily improving performance. Essentially, the agents spent more time debugging and increasing the complexity of their codes, but the actual test results did not yield much improvement.

MLRC-BENCH is designed to be a dynamic benchmark, continually updated with new competitions to reflect the evolving landscape of machine learning research. The hope is that it will serve as a valuable tool for the AI community, encouraging more rigorous and objective evaluations of AI’s research capabilities. As the paper states, balancing subjective judgments with concrete, performance-driven benchmarks is crucial for evaluating AI research agents.