Comprehensive Vendor-Neutral Reference Catalog Created for 84 Machine Learning Numeric Formats
A new arXiv preprint introduces a catalog of 84 numeric formats used in machine learning hardware, accompanied by six bit-exact conformance test packs covering formats such as FP8, BF16, and MXFP4. The work addresses a gap in shared reference material as numeric format proliferation has outpaced standardization efforts, making it difficult for engineers to diagnose silent divergences when porting models across different accelerators. The openly licensed catalog aims to serve as a vendor-neutral ruler for hardware and software implementers working toward interoperability.
A preprint submitted to arXiv on June 8, 2026 presents a structured catalog of 84 numeric formats spanning 13 families commonly encountered in machine learning hardware, including FP8 variants (E4M3 and E5M2), BF16, MXFP4, and microscaling block formats. The authors provide six self-contained, bit-exact conformance packs in JSON format, each with a SHA-256 fingerprint and a shared row schema designed to enable reproducible validation. A cross-walk to the IEEE P3109 v3.2.0 standard maps each conformance pack to its corresponding standards-track configured format, situating the work within ongoing standardization efforts. The packs are cross-validated against Google/JAX's ml_dtypes 0.5.4 library, with any divergences explicitly documented and attributed to spec-permitted interpretation gaps rather than errors. A shared anchor vector encoding the mathematical identity phi^2 + 1/phi^2 = 3 serves as a cross-pack sanity check. The authors explicitly frame the contribution as registry filling — it does not propose new formats, make model-accuracy claims, or favor any vendor's implementation. All artifacts are publicly available under open licenses (CC BY 4.0 for the paper, MIT for the code).
What's missing
As a preprint, this work has not yet undergone formal peer review. The paper does not discuss adoption pathways or whether major hardware vendors (e.g., NVIDIA, AMD, Intel) have been consulted or have indicated plans to use the catalog as a reference. The practical impact on reducing real-world divergence in deployed systems remains undemonstrated.
What different sources said
- arXiv cs.AICenter
An 84-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats
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