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The Numerical Fix Needed One More Property Test
Hypothesis tests reproduced two known softmax failures, found another edge case in the proposed repair and guided an implementation that also supports causal masking.
Summary
Hypothesis tests reproduced two known softmax failures, found another edge case in the proposed repair and guided an implementation that also supports causal masking.
Softmax and cross-entropy sit inside nearly every modern neural network, so rare numerical failures can travel widely. This paper encodes expected invariants as property-based tests and uses Hypothesis to reproduce two previously reported problems in implementations found across major machine-learning libraries. The same test suite exposes an additional edge case in the earlier repair. The authors then propose a more robust softmax implementation that passes their generated cases and supports causal-attention masking. The contribution is not a census of every installed library version; it is a compact demonstration that executable mathematical properties can uncover failures missed by example-based tests.
Why it matters
Hypothesis tests reproduced two known softmax failures, found another edge case in the proposed repair and guided an implementation that also supports causal masking.
Limits and context
- The contribution is not a census of every installed library version; it is a compact demonstration that executable mathematical properties can uncover failures missed by example-based tests.
Key claims
Hypothesis tests reproduced two known softmax failures, found another edge case in the proposed repair and guided an implementation that also supports causal masking.
Qualification: The contribution is not a census of every installed library version; it is a compact demonstration that executable mathematical properties can uncover failures missed by example-based tests.
Evidence: source-2026-10-28-003
Sources
- arXiv preprint 2610.36427, original v1arXiv · primary research
Corrections
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