AI research agents can propose, execute, and improve programs that solve machine learning tasks. However, executing each candidate program requires training and evaluating ML models, limiting how many
A world model predicts machine learning program outcomes to enable affordable test-time scaling for AI research agents, improving performance with fewer real executions.
AI research agents can propose, execute, and improve programs that solve machine learning tasks. However, executing each candidate program requires training and evaluating ML models, limiting how many candidates an agent can explore at each step. We introduce MLE-World, a world model finetuned from Qwen3.5-4B that predicts a program's terminal output (error traces, training logs, and validation scores) from its code and data description without executing it, together with a confidence score estimating the prediction's correctness. Because a prediction costs one inference call rather than a training run, MLE-World makes test time scaling (TTS) affordable for these agents. We build five TTS…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
MLE-World: Predicting Machine Learning Experiment Outcomes with a Learned World Model was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 1 check on 1 different day. Its best position was #29 of 30, reached on Oct 30, 01:28 UTC. It dropped off the list after Oct 30, 01:28 UTC.
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Not at the moment. It was last on the list on Oct 30, 01:28 UTC, after 1 day of trending.
#29 on Hugging Face · Daily papers, on Oct 30, 01:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 1 day (1 check) since.