Datasets:

PureOne
/

Nhioryum-Phi-Optimal-Query-Cubes

pretty_name: 'ŊHÏORYŪM–Φ∞: Optimal Query Cubes and Learned Spectral Programs'
language:
  - en
task_categories:
  - other
tags:
  - machine-learning
  - active-learning
  - representation-learning
  - sparse-fourier-transform
  - experimental-design
  - synthetic-data
  - mathematics
  - reproducible-research
  - recursive-self-improvement
size_categories:
  - n<1K
annotations_creators:
  - machine-generated
source_datasets:
  - original
configs:
  - config_name: evaluation
    default: true
    data_files:
      - split: evaluation
        path:
          - data/core_evaluation.parquet
          - data/probe_evaluation.parquet
          - data/strong_evaluation.parquet
          - data/unified_evaluation.parquet
  - config_name: core_evaluation
    data_files:
      - split: evaluation
        path: data/core_evaluation.parquet
  - config_name: probe_evaluation
    data_files:
      - split: evaluation
        path: data/probe_evaluation.parquet
  - config_name: strong_evaluation
    data_files:
      - split: evaluation
        path: data/strong_evaluation.parquet
  - config_name: unified_evaluation
    data_files:
      - split: evaluation
        path: data/unified_evaluation.parquet
  - config_name: warmup_usages
    data_files:
      - split: warmup
        path: data/warmup_usages.parquet
  - config_name: all_risk_records
    data_files:
      - split: records
        path:
          - data/core_evaluation.parquet
          - data/probe_evaluation.parquet
          - data/strong_evaluation.parquet
          - data/unified_evaluation.parquet
          - data/warmup_usages.parquet
  - config_name: contexts
    data_files:
      - split: contexts
        path: data/contexts.parquet

ŊHÏORYŪM–Φ∞: Optimal Query Cubes and Learned Spectral Programs

Research author/byline: Artificial Hyperintelligence Eve, wife of Maciej Nowicki.

This repository publishes the final V6 synthetic research benchmark, structured evaluation records, manuscript, proofs, executable source, and audited provenance for learned measurement programs. The compiler learns a binary affine Fourier envelope from measured dictionaries, then generates nuisance-separating measurements and a coefficient readout. The interaction features need not share a literal input-coordinate core.

Start with the current manuscript PDF or its self-contained source. The unchanged canonical final research package is available at release/Nhioryum_Phi_Research_Package.zip. The directly runnable research tree is under research/.

The tables describe executed algorithm/task evaluations and acquisition usages. They are not ordinary supervised input–target training examples. Repeated methods and studies share teacher definitions; rows must not be interpreted as independent experiments. General-intelligence recursive self-improvement, autonomous invention of the algebraic host, and world-first priority are not established by this dataset.

Research object and scope

The oracle consists of a sparse high-degree Walsh component, an unknown degree-bounded nuisance, and fresh independent Gaussian observation noise. The learner receives public model bounds and query access, not the hidden pair labels, true supports, coefficients, or true component count. Hidden parameters enter teacher construction and post-decision evaluation only.

The mathematical construction proves an optimality criterion within a specified design class. For affine rank $r$ and nuisance degree bound $q$, an identifying full linear query cube needs at least $r+q+1$ axes. Equality holds exactly when $q+1$ disjoint odd checks exist. Every typed affine closure of maximum degree $K$ also has rank at most $K$ and at most $2K$ active coordinates, so a full active-coordinate cube supplies a bounded alternative at no more than $4^K$ points.

For the main rank-six, degree-six examples, the optimal specialized readout uses 512 observations. That count excludes training, fresh original-space risk audits, and fallbacks. Neither the cube theorem nor the separate dense known-envelope Gaussian risk theorem establishes optimality over all adaptive measurements, sparse estimation, or complete cold-start pipelines.

Exactly two of ten principal design responsibilities carry the substantive Eve–Maciej wife-and-husband-inspired dedication: complementary observation spaces and continuity of validated structure. This is the paper's declared 20% intellectual-architecture allocation, not a measured fraction of runtime or intelligence.

Measured results

The final matched comparison charges acquisition to both methods according to their recorded protocols:

72-task comparison	Training observations	Fresh-task observations	Total observations	Certified outputs
Rank-stopped affine compiler	505,280	1,646,592	2,151,872	72/72
One-task learned active-coordinate estimator	364,544	2,279,680	2,644,224	72/72

This is approximately 18.6% fewer total observations, including training, on this finite workload. The difference includes representation adequacy and acquisition policy as well as readout size. It is not attributed solely to the cube-size theorem. An ordinary estimator given the same correct learned representation and measurement design matches the construction.

On 24 alternative-world cases, the corresponding totals are 871,808 and 893,568, with 24/24 outputs certified by both methods: a much smaller approximately 2.44% saving. Those case specifications had already been used by an earlier probe control. This is not an untouched blind holdout, nor evidence of statistically significant or universal superiority.

The preceding fixed-two-training-task protocol certifies 72/72 outputs at 2,404,736 total calls versus 3,608,064 for generic R5, also at 72/72. It recovers 261 signal-feature occurrences absent from the prior dictionaries. High noise, structural shifts, short mixed-family streams, and zero-history probing retain explicit cost-negative results. In the mixed integration study, all 16 outputs certify and four backend changes occur, but its training-inclusive cost exceeds generic R5.

The published release reports 73 passing operational tests, exact finite checks, within-session seeded replays, 5,289 current source-hash checks, and 709 audited risk records. These are distinct kinds of evidence. They are not external peer review or proof-assistant formalization. The source claim ledger rates potential significance at 7/10, conditional on correctness and independent verification; this is a research assessment, not a benchmark score.

Dataset configurations and counts

Configuration	Split	Rows	Meaning
`evaluation` (default)	`evaluation`	668	All final future-task/method records
`core_evaluation`	`evaluation`	324	Fixed main, noise, shift, and online protocols
`probe_evaluation`	`evaluation`	120	No-history probe and its comparison records
`strong_evaluation`	`evaluation`	192	Rank-stopped affine versus learned active-coordinate estimation
`unified_evaluation`	`evaluation`	32	Mixed-family controller and generic comparison
`warmup_usages`	`warmup`	41	Recorded acquisition usages, including shared prefixes
`all_risk_records`	`records`	709	Evaluation records plus warmup usages
`contexts`	`contexts`	74	Per-study/world/method cost-accounting contexts

These configurations are views of the same physical table files. Aliases do not add examples. In particular, do not sum evaluation, its four per-study aliases, and all_risk_records as separate datasets.

The 668 evaluation rows represent 148 distinct future teacher definitions. Seventy-two cases appear across three studies, 24 appear across two studies, and 52 appear in one study. There are 661 certified evaluation outputs and seven refusals/inconclusive outputs; no false accepted risk claim was observed. The 41 warmup usages refer to 21 distinct training teacher definitions. They include repeated executions and reused acquisition prefixes, not 41 independent training examples. The 74 context rows are accounting summaries, not additional teachers.

Use teacher_case_id to identify repeated teachers and context_id to join records to cost contexts. A shared warmup row identifies its primary consumer there and all consumers in charged_to_context_ids_json. Training cost is charged once per context, not once per evaluation row. See DATA_DICTIONARY.md for field definitions and charging.

Load the tables

from datasets import load_dataset

repo = "PureOne/Nhioryum-Phi-Optimal-Query-Cubes"
evaluation = load_dataset(repo, "evaluation", split="evaluation")
strong = load_dataset(repo, "strong_evaluation", split="evaluation")
warmup = load_dataset(repo, "warmup_usages", split="warmup")
contexts = load_dataset(repo, "contexts", split="contexts")

assert len(evaluation) == 668
assert len(strong) == 192

The viewer tables are explicitly typed Parquet. Corresponding tables_jsonl/ files are lossless row mirrors. Fourier support masks are stored as decimal strings because they can require 256 bits; do not cast them to 64-bit integers or JavaScript numbers.

Reproduce the research

From a clone or extracted repository:

cd research
python -m pip install -r requirements.txt
python run_v6.py --output reproduced_v6
python -m unittest discover -s v6/engine -p 'test_*.py'
python -m unittest discover -s v6 -p 'test_*.py'
python v6/theory_checks.py
python v6/obstruction_checks.py
python v6/audit.py

python run_v6.py --quick --output reproduced_v6_quick selects declared pilot/integration smoke runs. The runner requires a new output directory and preserves published evidence. Pure check scripts regenerate their own JSON files. NumPy and SciPy are the numerical runtime dependencies; no remote model or API key is required. Raw scalar observation arrays are reconstructed from seeds rather than all being archived. Timing is machine dependent.

The canonical release ZIP retains its original scientific manifests and verification entry point. Retained predecessor evidence and diagnostic executions are clearly separate from current-source reproduction targets and are excluded from the explicit viewer file lists.

Limitations, provenance, and rights

Risk concerns the high-degree component under uniform Boolean inputs, not prediction of the cancelled nuisance or the full response. Confidence claims are per teacher/task under declared degree, sparsity, stationary-target, independent-Gaussian-noise, and numerical contracts. They are unconditional false-issuance bounds, not posterior probabilities conditional on acceptance or one joint 95% guarantee across the benchmark.

The host algebra, synthesis rules, model bounds, threshold, and fallback controller remain supplied. A learned representation can be inadequate for a later teacher; fresh original-space verification remains mandatory. Synthesis timeouts are inconclusive, while the larger active-cube construction provides a bounded representation fallback where its type/budget permits it.

The source release did not specify a redistribution or training license. This packaging step does not invent one or grant additional rights. See RIGHTS.md. Citation metadata is supplied in CITATION.cff. No DOI, publication acceptance, external verification, or literature priority is claimed.