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arXiv AI By Qusay Muzaffar, David Levin, Michael Werman

One-Shot Localisation of the Global Minimum of a Noisy One-Dimensional Function: A Controlled Comparison of an Iterative Neural Minimizer, Set Transformers and Classical Estimators

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The paper introduces the Neural Function Minimizer (NFM), a one-shot method that iteratively moves a position across a one‑dimensional domain to locate the global minimum from twenty noisy samples. In a controlled benchmark, the NFM matches Set Transformers on location error but achieves lower regret, especially with mixture heads, and outperforms classical estimators on unseen functions. The authors plan to release the benchmark, models, and harness for further research.

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