MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI

Hugging Face Daily Papers Papers
benchmark ai-agents machine-learning research evaluation

Summary

This paper introduces MLS-Bench, a benchmark designed to assess whether AI systems can invent generalizable and scalable machine learning methods rather than just performing engineering tuning.

Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities in reasoning, coding, and engineering tasks, it is increasingly important to understand whether they can discover such methods rather than only apply existing ones. We introduce MLS-Bench, a benchmark for evaluating whether AI systems can invent generalizable and scalable ML methods. MLS-Bench contains 140 tasks across 12 domains, each requiring an agent to improve one targeted component of an ML system or algorithm and demonstrate that the improvement generalizes across controlled settings and scales. We find that current agents remain far from reliably surpassing human-designed methods, and that engineering-style tuning is easier for them than genuine method invention. We further study the effects of test-time scaling, adaptive compute allocation, and context provision on agents' discovery performance, together with case studies of their behavior. Our analyses suggest that the bottleneck is not only in proposing new methods, but also in the scientific insight needed to plan, validate, and scale claims about them. More search, compute, or context alone does not remove this bottleneck. We build and maintain a community platform for cumulative and comparable iteration, and release the data and code at https://mls-bench.com.
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Abstract

Current AI agents struggle to invent generalizable and scalable machine learning methods, relying more on engineering tuning than true method discovery, with performance bottlenecks stemming from scientific insight rather than computational resources.

Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. Aslarge language modelsdemonstrate advanced capabilities in reasoning, coding, and engineering tasks, it is increasingly important to understand whether they can discover such methods rather than only apply existing ones. We introduceMLS-Bench, a benchmark for evaluating whether AI systems can invent generalizable andscalable ML methods.MLS-Benchcontains 140 tasks across 12 domains, each requiring an agent to improve one targeted component of an ML system or algorithm and demonstrate that the improvement generalizes across controlled settings and scales. We find that current agents remain far from reliably surpassing human-designed methods, and thatengineering-style tuningis easier for them than genuinemethod invention. We further study the effects oftest-time scaling,adaptive compute allocation, andcontext provisionon agents’ discovery performance, together with case studies of their behavior. Our analyses suggest that the bottleneck is not only in proposing new methods, but also in thescientific insightneeded to plan, validate, and scale claims about them. More search, compute, or context alone does not remove this bottleneck. We build and maintain a community platform for cumulative andcomparable iteration, and release the data and code at https://mls-bench.com.

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