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New paper reveals numerical issues in ML libraries, proposes fixes

By PulseAugur Editorial · [1 sources] ·

A recent paper by Barron et. al. identified numerical issues in the softmax and cross-entropy functions used across common machine learning libraries like PyTorch and JAX. Researchers demonstrated that property-based testing, specifically using Hypothesis, can uncover these problems. Their work also proposes a more robust softmax implementation that addresses these issues and supports causal attention masking, with the code available on GitHub. AI

IMPACT Highlights the importance of rigorous testing for numerical stability in core ML functions, potentially leading to more reliable AI models.

RANK_REASON The cluster contains a research paper detailing numerical issues and proposed solutions for machine learning library functions. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New paper reveals numerical issues in ML libraries, proposes fixes

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The cluster contains a research paper detailing numerical issues and proposed solutions for machine learning library functions. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Property-Based Testing for Robust Softmax and Cross-Entropy

    A recent paper of Barron et. al. (2026) identified two numerical issues with the softmax and cross-entropy functions in virtually all standard machine learning libraries (e.g., PyTorch and JAX). We show that these issues can be recovered through simple property-based tests in Hyp…

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