ML that doesn’t crash at 1/0.
ZeroProofML keeps models stable when math gets tricky—no hacks, no guesswork. Always‑defined math. Stable training. Deterministic runs.
Stable ML by Design
Always‑defined math, stable learning near trouble spots, and deterministic runs that reproduce across machines.
Always‑defined math
Calculations return meaningful results for every input—avoid NaNs and silent failures.
How it worksStable near trouble spots
Protects gradients and keeps training informative near edge cases and asymptotes.
See detailsDeterministic by default
Seeded runs produce the same results. Reproducible pipelines and audit‑ready logs.
Get startedMaking machine learning safe where math gets hard.
ZeroProofML began with a simple question: Why do models still fall apart on edge cases we can describe in math? We built a small, robust set of components that keep learning stable—even when divisions get tiny or functions blow up. Always‑defined math, safe normalization, and deterministic runs so you can train it safely and deploy with confidence.
Read the docs
Ship models that don’t crash. Get started now
Ready to stop firefighting numeric issues? Try safe‑by‑default layers and normalization.
GithubQuestions, answered
A quick primer on ZeroProofML’s approach and what to expect.
Does this change my model’s math?
No. Where your function is well‑defined, we leave it alone. We only add safe behavior where standard math breaks so training can continue.
Will training be slower?
Slightly, due to safety checks. But you avoid failed runs and retries—so you typically finish faster overall.
What does ‘always‑defined math’ mean?
Every operation returns a meaningful value: REAL numbers, ±∞ when appropriate, and a special Φ (‘nullity’) for undefined‑but‑handled cases—no NaNs or crashes.
What will I see in practice?
Fewer crashes and restarts during training, lower error where it used to spike near cutoffs/resonances, and reproducible results when you set a seed.
How do I get started?
Install, swap in a rational layer and safe normalization for brittle spots, then run a quick demo or your own pipeline with determinism enabled.
How do I contact you?
Email hello@zeroproofml.com for general questions or dome@zeroproofml.com for collaborations and implementation support.
From The Blog
Why Your Neural Network Can't Learn 1/x (And What We Did About It)
Why smooth activations create dead zones near poles—and how rational layers with tagged infinities fix it for robotics IK and beyond.
Get in touch
Location
Budapest, Hungary
hello@zeroproofml.com
dome@zeroproofml.com