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dict
best_phi_approximation
bool
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timestamp[s]
description
string
format_bits
int64
format_family
string
format_name
string
format_spec
dict
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bool
phi_distance
float64
sacred_ok
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{ "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:

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/ (.t27 SSOT 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 identity
  • bit_layout / format_spec -- exact field widths
  • seal -- SHA-256 of the row payload for tamper-evidence
  • formal_proof (where applicable) -- Coq lemma name in coq/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-corona 80-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-catalog but 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_error only. 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 goldenfloat tag are designed around the closed-form rule e = 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

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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Papers for playra/numeric-conformance-packs