benchmark_metrics dict | best_phi_approximation bool | created_at timestamp[s] | description string | format_bits int64 | format_family string | format_name string | format_spec dict | is_primary bool | phi_distance float64 | sacred_ok bool | schema_version int64 | seal string | test_vectors list | updated_at timestamp[s] | validated_at timestamp[s] | vector_name string | verdict string | version string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{
"memory_savings": 0.625,
"mse_vs_fp32": null,
"speedup_vs_fp32": null,
"use_cases": [
"high_precision_quantization",
"attention_matrices",
"critical_path_weights"
]
} | true | 2026-04-06T15:54:08 | GoldenFloat12 Conformance Vectors — NUMERIC-STANDARD-001 (Agent 12) | 12 | GF12 | GF12 | {
"exp_bias": 7,
"exp_bits": 4,
"exp_mant_ratio": 0.5714285714,
"mant_bits": 7,
"memory_ratio_vs_fp32": 0.375,
"sign_bits": 1
} | false | 0.046605 | true | 2 | sha256:2f3e1dbb4fc3c235f0a08067294d33844976f2787a9dc72a906eaf4dd5acd0df | [
{
"description": "Positive zero encodes to 0x000",
"expected": {
"decoded": 0,
"raw": 0,
"tolerance_abs": 0
},
"input": {
"value": 0
},
"name": "zero_positive",
"verdict": "CLEAN"
},
{
"description": "1.0 encodes to exp=7, mant=0",
"expected": {
... | 2026-04-06T15:54:08 | 2026-04-06T15:54:08 | gf12 Vectors | CLEAN | 1.0 |
YAML Metadata Warning:The task_ids "other" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Numeric Conformance Packs (Trinity S3AI / t27)
Version: v2.0 (2026-06-10).
License: MIT.
Maintainer: Trinity S3AI -- admin@t27.ai -- ORCID 0009-0008-4294-6159.
Mirror of: gHashTag/t27 conformance/ and specs/numeric/ subtrees.
Companion dataset: playra/numeric-format-catalog (the 80-format ruler this pack instantiates).
Linked papers:
- Anchor preprint (GoldenFloat identity): arXiv:2606.05017
- Catalog preprint (84-format ruler): arXiv:2606.09686 -- HF paper page
TL;DR
Bit-exact conformance vectors for the GoldenFloat (GF) family of phi-structured floating-point formats, plus two reference packs (phi_identity, phi_ratio) that exercise the canonical phi^2 + phi^-2 = 3 identity. The packs let any third-party kernel implementer check binary-level conformance with abs_error instead of "looks close in plots".
Quick start
from datasets import load_dataset
# Pull a single pack
gf16 = load_dataset("playra/numeric-conformance-packs", "gf16", split="vectors")
print(gf16.column_names)
# Verify round-trip on one vector
sample = gf16[0]
hex_bits = sample["bits_hex"]
expected = sample["f32_value"]
# Your decoder under test:
recovered = your_gf16_decoder(int(hex_bits, 16))
abs_error = abs(recovered - expected)
assert abs_error <= sample["tolerance"], f"GF16 conformance violated: {abs_error}"
The full call-and-response contract is documented per pack in manifest.json.
Pack inventory
| Config | File | Lines | SHA-256 (8) | Purpose |
|---|---|---|---|---|
gf4 |
gf4.jsonl |
1 | dd264387 |
GF4 reference vectors |
gf8 |
gf8.jsonl |
1 | 44e27c75 |
GF8 reference vectors |
gf12 |
gf12.jsonl |
1 | 682e824d |
GF12 reference vectors |
gf16 |
gf16.jsonl |
1 | a7935733 |
GF16 reference vectors (frozen silicon) |
gf20 |
gf20.jsonl |
34 | 8bec16f1 |
GF20 reference vectors |
gf24 |
gf24.jsonl |
34 | 546019d0 |
GF24 reference vectors |
gf32 |
gf32.jsonl |
37 | 0dbb3a9c |
GF32 reference vectors |
goldenfloat_family |
goldenfloat_family.jsonl |
25 | a6660f43 |
Family-level invariants and cross-checks |
phi_identity |
phi_identity.jsonl |
3 | 88563e4e |
phi^2 + phi^-2 = 3 reference points |
phi_ratio |
phi_ratio.jsonl |
1 | 90a183f4 |
Canonical phi as f64 with hex tolerance |
Full SHA-256 manifest: SHA256SUMS.txt. Machine-readable schema and per-pack manifest: manifest.json.
Provenance
This dataset is a mirror, not a primary source. The primary source of truth is the t27 repository:
- Specs:
gHashTag/t27->specs/numeric/(.t27SSOT files) - Conformance generators:
gHashTag/t27->conformance/+tools/gen_*.py - Frozen silicon anchor: GF16 is validated end-to-end against the TTGF26a / GF180MCU tape-out (35/35 vectors at 323 MHz, see arXiv:2606.09686 Section 6).
Every JSONL row carries:
format_name,format_bits,format_spec-- canonical identitybit_layout/format_spec-- exact field widthsseal-- SHA-256 of the row payload for tamper-evidenceformal_proof(where applicable) -- Coq lemma name incoq/Kernel/*
How this fits the wider ruler
These packs sit at Tier 1 of a three-tier numeric-format corpus:
- Tier 0 SSOT:
specs/numeric/formats_catalog.t27(machine-checked, 83 rows live) - Tier 1 Portable JSON packs (this dataset)
- Tier 2 Silicon-validated ROM:
tt-trinity-corona80-format ROM + 19 RTL decoders + 5-layer dossier (TTGF26a / GF180MCU tape-out)
The companion dataset playra/numeric-format-catalog carries the full 80-format catalog with cluster, bit-width, and phi-distance metadata.
Considerations for using the data
Recommended uses
- Verifying a third-party GF or phi-arithmetic kernel against a fixed reference
- Cross-checking custom GoldenFloat decoders during silicon bring-up
- Teaching: bit-exact rounding behaviour for non-IEEE phi-structured formats
- Audit and reproducibility studies of phi-structured arithmetic
Out of scope
- IEEE P3109 OFP8 -- cross-walk is documented in
p3109_crosswalk.json(mapping only) but not a primary distribution here - NF4 / NF8 (NormalFloat) -- adjacent quantisation formats, not covered
- MXFP4 / MXFP6 -- OCP mixed-precision wrappers, see arXiv:2606.09686 Section 4 for the cross-walk
- Posit / Takum / LNS -- alternative number systems are catalogued in
playra/numeric-format-catalogbut no conformance pack here - Mixed-precision GEMM -- this is a per-format conformance dataset, not an end-to-end kernel benchmark
Known limitations
- Pack size is uneven by design. Several packs ship 1 row (anchor); others ship 34-37 rows (sweep). The number of rows reflects how the format is tested in the kernel, not its importance.
abs_erroronly. PCC, FID, and other distributional metrics are explicitly out of scope; this is a pointwise conformance dataset.- GF dominance. Reflects the maintainer's research focus. For SOTA AI quantisation formats (NF4, MXFP4, posit), this dataset is not a substitute for OCP or open-compute reference implementations.
- Single-language schema. All vectors and manifest are English-only. No localisation.
- No social-bias dimension. Numeric format conformance is not a fairness-sensitive task; if you're using these vectors as part of a downstream model evaluation, fairness considerations live at the model level, not here.
Known biases
- Equal weight per cluster. The pack distribution mirrors the maintainer's research priorities; it is not a representative sample of the AI accelerator format population.
- Phi-structured emphasis. All formats with the
goldenfloattag are designed around the closed-form rulee = round((N-1)/phi^2). This is by construction, not a discovered property.
Citation
@misc{vasilev2026numericrulercatalog,
title = {Eighty Formats and a Ruler: A Comprehensive Numeric Format Catalog
for Phi-Structured and IEEE Floating-Point Arithmetic},
author = {Vasilev, Daniil and {Trinity S3AI}},
year = {2026},
eprint = {2606.09686},
archivePrefix = {arXiv},
primaryClass = {cs.AR},
url = {https://arxiv.org/abs/2606.09686}
}
@misc{vasilev2026goldenfloatidentity,
title = {GoldenFloat: A Phi-Structured Floating-Point Family with Closed-Form
Field Widths from the Identity Phi^2 + Phi^-2 = 3},
author = {Vasilev, Daniil and {Trinity S3AI}},
year = {2026},
eprint = {2606.05017},
archivePrefix = {arXiv},
primaryClass = {cs.AR},
url = {https://arxiv.org/abs/2606.05017}
}
@misc{trinitys3ai2026numericpacks,
title = {Numeric Conformance Packs},
author = {{Trinity S3AI}},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/playra/numeric-conformance-packs}
}
Maintenance and changelog
| Date | Version | Change |
|---|---|---|
| 2026-04-07 | v1.0 | Initial 10-pack release |
| 2026-06-10 | v2.0 | Updated with audit-disclosed schema, SHA-256 manifest, anchor preprints |
| 2026-06-13 | v2.1 | Added catalog preprint cross-link (arXiv:2606.09686), README upgrade per FAIR / Datasheets / Data Cards |
Update cadence. No fixed cadence. New packs are added when (a) a new GF rung is verified, or (b) a new conformance contract is requested via gHashTag/t27 issues. Existing rows are immutable; if a format spec changes, the row is replaced and the SHA-256 manifest tracks the diff.
Contact. admin@t27.ai -- async-only, ASCII-only.
Machine-readable metadata
- Croissant JSON-LD:
croissant.jsonat repo root (MLCommons spec 1.0) - YAML metadata: at the top of this file, machine-parseable per HF Hub spec
- SHA-256 manifest:
SHA256SUMS.txt - P3109 cross-walk (read-only mapping):
p3109_crosswalk.json
Identity stack
- Maintainer: Vasilev Daniil (Trinity S3AI)
- Email: admin@t27.ai (sole canonical contact)
- ORCID: 0009-0008-4294-6159
- GitHub: gHashTag
- HF org: trinity-s3ai
- Anchor identity:
phi^2 + 1/phi^2 = 3
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