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Prof-K: Fast, Scalable Top-k Selection with Probabilistic Guarantees.

Tadeusz Dziarmaga, Witold Sikora, {\L}ukasz Struski, Jacek Tabor, Marcin Mazur· August 14, 2026 View original

Key takeaways

  • Prof-K is a fast, scalable, and distribution-agnostic top-k selection algorithm.
  • It uses a single-pass filtering procedure with probabilistic correctness guarantees.
  • The algorithm achieves significant speedups over existing methods, especially for large datasets.
  • It is robust to adversarial or heavy-tailed inputs, unlike many approximate methods.

Who benefits

Data ScienceAI DevelopmentFinTechE-commerce

Summary

Prof-K is a new, efficient algorithm for top-k selection that offers probabilistic correctness guarantees and performs a single-pass filtering procedure. It achieves significant speedups over existing methods, especially for large datasets and small-to-moderate k values, without relying on input distribution assumptions.

A new algorithm named Prof-K has been introduced, designed to address the inefficiencies of existing top-k selection methods, particularly as data volumes continue to grow. This innovative approach offers a fast, scalable, and distribution-agnostic solution, providing robust probabilistic guarantees for its correctness. Unlike prior techniques that struggle with large-scale data or adversarial inputs, Prof-K maintains its performance and reliability. The core of Prof-K involves a single-pass filtering process. It begins by taking a small random sample to estimate an adaptive threshold. Subsequently, all input elements are streamed once into a compact buffer. An exact top-k routine is then applied to this buffer, ensuring the recovery of the true top-k elements with a user-specified high probability. Empirical evaluations show Prof-K delivering 1.5x to 10x speedups compared to highly optimized implementations like PyTorch's topk and RadiK, with its most significant gains observed in scenarios with large datasets and small-to-moderate k values.

Why it matters

Efficient top-k selection is fundamental to many data-intensive applications, from databases to machine learning. Prof-K offers a robust, faster, and more scalable solution, potentially reducing computational costs and improving performance in critical workloads.

How to implement this in your domain

  1. 1Benchmark: Compare Prof-K's performance against current top-k implementations in your data processing pipelines.
  2. 2Integrate: Explore integrating Prof-K into systems requiring efficient top-k selection, such as sparse activation processing or attention pruning in ML models.
  3. 3Optimize: Adjust the user-specified epsilon parameter to balance accuracy and speed according to application requirements.
  4. 4Evaluate: Test its robustness with various input distributions, including adversarial or heavy-tailed data.

Original post by Tadeusz Dziarmaga, Witold Sikora, {\L}ukasz Struski, Jacek Tabor, Marcin Mazur

"arXiv:2608.12573v1 Announce Type: new Abstract: Top-k selection is a fundamental computational primitive with applications spanning databases, information retrieval, signal processing, and modern machine learning workloads, including sparse activations and attention pruning. As d…"

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Originally posted by Tadeusz Dziarmaga, Witold Sikora, {\L}ukasz Struski, Jacek Tabor, Marcin Mazur on X · view source

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