data/README.md

Experiment-run dataset

Subset/configuration: experiment_runs. Split: test. File: experiment_runs.jsonl. Five rows summarize the five recorded runs of the exact finite-family AXIOMESH experiments. Values are extracted from the unchanged results/results.json and results/replications/<seed>/results.json artifacts.

The integer seeds 20261007 through 20261011 are RNG labels, not run dates. Each row summarizes a run with many correlated tests; it is not an independent real-world task, training example, or general-intelligence result. The architecture and strong pooled learner tie the principal prediction benchmark.

Field	Type	Meaning
seed	integer	RNG seed in the source run
core_version	string	Scientific implementation version
learned_test_words	integer	Held-out words in the supplied learned family
weave_correct	integer	Correct proposed-pipeline outputs for those words
pooled_baseline_correct	integer	Correct strong conventional pooled outputs
pooled_baseline_tie	boolean	Equality of the two correct-output counts
erasure_trials	integer	Total trials over the source erasure configurations
false_erasure_certificates	integer	Incorrect exact recovery certificates in those trials
identifiability_trials	integer	Exhaustive finite-fiber checks
incorrect_identifiability_certificates	integer	Incorrect certificates in the finite-fiber checks
frozen_closure_test_words	integer	Sum of words in frozen-closure checks
recursive_fracture_test_words	integer	Words checked after recursive reconstruction
recursive_fracture_correct	integer	Correct outputs in the recursive-fracture check
source_result_file	string	Repository-relative provenance path
evidence_scope	string	Scope reminder for each row

The erasure trials include honest abstentions when insufficient symbols survive; zero false certificates does not mean that every erased state was recovered. Timing data and large program structures remain in the source artifacts rather than being treated as comparable tabular measurements.

Load locally

python -m pip install -r requirements-dataset.txt

From the repository root:

from datasets import load_dataset

runs = load_dataset("json", data_files={"test": "data/experiment_runs.jsonl"}, split="test")
assert len(runs) == 5

After dataset publication, use the repository ID and explicit configuration as shown in the root dataset card. No custom dataset loading script is required.

This is synthetic mathematical evaluation evidence produced by the bundled reference implementation, not human training data. It covers supplied finite operator families and does not establish neural RSI or unrestricted reconstruction. The repository owner's existing unselected reuse-license status is preserved.