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0001d8fa-3147-4fd0-a49d-61614c52d9ac
easy
CodeReclaimers
2026-08-20 15:21:07.916546+00:00
succeeded
ecdc5bc3549f21ec4b5703f65802288e92bc05cd2c1587f9346ab4e680aba37d
42,274
null
"""v39_keyed: keyed-modulus CRT learner — Run A of T1_CERT_PLAN_20260819. HYPOTHESIS A (alias modulus): h1's displayed N is a stable per-identity alias phi(M) of a hidden modulus M <= 2^20 from enumerated factor pairs (<=10-bit primes), while x and y are displayed plainly and the step closes per prime channel (squarin...
{ "id": "0001d8fa-3147-4fd0-a49d-61614c52d9ac", "created_at": "2026-08-20 15:21:07.916546+00:00", "db_md5": "4734e439309d100106007d6b654ccab0", "submitter": "CodeReclaimers", "github_login": "CodeReclaimers", "run_id": "9824eccf-75ad-45fa-86fd-90000b08b1dc", "tier": "easy", "dataset_id": "e5", "status...
{ "score": { "mean_loss": 1.6747209675214556, "primary_metric": "mean_exact_accuracy", "num_measurements": 2, "mean_exact_accuracy": 0.47916666706403094 }, "seeds": [ { "seed": 74, "evaluation": { "ood": { "loss": 2.2111226555477876, "example_count": 600...
000228af-ba9c-4313-a87f-74121f716157
easy
liam-gb
2026-08-11 10:17:03.181174+00:00
succeeded
a52fd23b420d06c1ea43204544d5e9ffbcb40ea72e22fa95b26f8d1eaa5e17e6
12,304
null
"""rns_critical_uni: critical-path loop with uniform per-loop supervision. Each field value enters as its residue phase on a bank of small odd prime circles via fixed sinusoidal feature maps over digit significance (omega[s] = 2*pi*(10^s mod p)/p) — carry-free by construction. Phases are resolved against fixed unit-ci...
{ "id": "000228af-ba9c-4313-a87f-74121f716157", "created_at": "2026-08-11 10:17:03.181174+00:00", "db_md5": "4e4e60e58520faa9b1efeba2156ec5ae", "submitter": "liam-gb", "github_login": "liam-gb", "run_id": "15476376-a505-4f85-ac7e-e1c77ec3e5bb", "tier": "easy", "dataset_id": "e1", "status": "succeeded"...
{ "score": { "mean_loss": 5.046521425247192, "primary_metric": "mean_exact_accuracy", "num_measurements": 2, "mean_exact_accuracy": 0.0800000000745058 }, "seeds": [ { "seed": 74, "evaluation": { "ood": { "loss": 7.268509864807129, "example_count": 100, ...
0009d371-ce6b-4cb1-8742-acab0548134c
easy
jordanrubin
2026-08-06 19:23:31.320469+00:00
succeeded
2214473aabeee3e5a3252928aedd50993436405bf900fc579ecc4ef88811f4f4
14,788
null
"""Autonomous digit-state recurrent Transformer for repeated modular squaring. The model is deliberately organized around one learned transition: p_0 = right_aligned_decimal_digits(x) p_{k+1} = F_theta(p_k, decimal_digits(N)) The same two-block Transformer cell is applied exactly T times. T is used only as ...
{ "id": "0009d371-ce6b-4cb1-8742-acab0548134c", "created_at": "2026-08-06 19:23:31.320469+00:00", "db_md5": "f917ba4387feee357d1f6822e4a7825f", "submitter": "Jordan Rubin", "github_login": "jordanrubin", "run_id": "7a653879-d845-4735-89f6-8d24230933d3", "tier": "easy", "dataset_id": "e3", "status": "s...
{ "score": { "mean_loss": 2.1296750745907396, "primary_metric": "mean_exact_accuracy", "num_measurements": 2, "mean_exact_accuracy": 0.0018749999580904841 }, "seeds": [ { "seed": 74, "evaluation": { "ood": { "loss": 2.1309655856123277, "example_count": 8...
000a3457-d237-4459-bed2-b08371ee3d12
easy
sapient-sapiens
2026-08-19 19:24:48.910974+00:00
succeeded
daa4fc1f1821b95b049e62d1602f10121592d66b9efbbb3e78e141bca4403b2b
33,252
null
"""Depth-quantized recurrence with a mixture-over-depth objective. The recurrent state is a bank of right-aligned digit slots. Every operator application is followed by a soft quantization back onto the token simplex, and the digit logits produced by that quantization are the answer logits at that depth. There is no s...
{ "id": "000a3457-d237-4459-bed2-b08371ee3d12", "created_at": "2026-08-19 19:24:48.910974+00:00", "db_md5": "2553f964fd1803486430c2690edf5c49", "submitter": "Ertondy", "github_login": "sapient-sapiens", "run_id": "bc701422-a4a4-4e0b-a61a-aa3779c48292", "tier": "easy", "dataset_id": "e3", "status": "su...
{ "score": { "mean_loss": 2.2663152426947795, "primary_metric": "mean_exact_accuracy", "num_measurements": 2, "mean_exact_accuracy": 0.008125 }, "seeds": [ { "seed": 74, "evaluation": { "ood": { "loss": 2.276738232607394, "example_count": 800, ...
000c7da0-a4e9-4f0d-ac6d-ceba3bbd79e3
easy
karanganesan
2026-08-06 05:45:58.399132+00:00
succeeded
35bb515be06389766e7968d9741d65099fd39cb897f018b8a7b9b4d9e5188a5a
25,616
null
"""Parametric looped-transformer family (P1). One weight-tied transformer block applied k times in latent space. Config flags cover four P1 families with one file: looped recall=0 gated=0 tfilm=0 plain weight-tied loop looped-recall recall=1 re-inject the input embedding each ...
{ "id": "000c7da0-a4e9-4f0d-ac6d-ceba3bbd79e3", "created_at": "2026-08-06 05:45:58.399132+00:00", "db_md5": "853fe7df67aa06b7473fbcdd5ee2d312", "submitter": "Karan Ganesan", "github_login": "karanganesan", "run_id": "9ce87288-351f-43fc-9286-32c267afb2c7", "tier": "easy", "dataset_id": "e1", "status": ...
{ "score": { "mean_loss": 3.7291706800460815, "primary_metric": "mean_exact_accuracy", "num_measurements": 2, "mean_exact_accuracy": 0.011666666716337204 }, "seeds": [ { "seed": 74, "evaluation": { "ood": { "loss": 3.6878952980041504, "example_count": 10...
0019ad4c-eb58-4195-91c7-8648d5bcbcb6
easy
ArkinDharawat
2026-08-15 20:08:59.860054+00:00
succeeded
b3a3fc7ccd6a67634a1aaadb13d50e511a3f9ba42651a0b7a87cef3c72bf10ef
7,633
null
"""Universal-Transformer-style looped block, v2: step embed + pos offset + LR schedule + 6 loops. Extends ``looped_ut_padded`` with four ideas drawn from Graves 2016 (ACT), Dehghani et al. 2018 (Universal Transformer), and Merrill & Sabharwal 2025 (log-depth transformers): 1. **Learned per-step (timestep) embedding**...
{ "id": "0019ad4c-eb58-4195-91c7-8648d5bcbcb6", "created_at": "2026-08-15 20:08:59.860054+00:00", "db_md5": "df88b48f30c8035a9269455deffdd66b", "submitter": "Arkin Dharawat", "github_login": "ArkinDharawat", "run_id": "d7867b75-a51b-4a0b-8db9-6c11320331f7", "tier": "easy", "dataset_id": "e3", "status"...
{ "score": { "mean_loss": 2.240777682338874, "primary_metric": "mean_exact_accuracy", "num_measurements": 2, "mean_exact_accuracy": 0.009375000055879355 }, "seeds": [ { "seed": 74, "evaluation": { "ood": { "loss": 2.2808395810221542, "example_count": 800...
001d49d5-358e-4e45-9cab-9a698d534eb3
easy
mnida
2026-08-25 22:18:55.230889+00:00
succeeded
a18891d3780d413e19103a57d2fe22a3de5a430300e068f22fb0df54e9f2b708
15,662
null
"\"\"\"Large quadratic looped Transformer with immutable direct-N-blind routes.\"\"\"\n\nfrom __futu(...TRUNCATED)
"{\n \"id\": \"001d49d5-358e-4e45-9cab-9a698d534eb3\",\n \"created_at\": \"2026-08-25 22:18:55.230(...TRUNCATED)
"{\n \"score\": {\n \"mean_loss\": 2.1489941186962733,\n \"primary_metric\": \"mean_exact_acc(...TRUNCATED)
001e4315-d76b-43da-8a88-0f8a9d2e3f95
easy
nikolageorgiev2000
2026-08-04 15:19:10.646519+00:00
succeeded
bcb781bfdf7ba38a0a37eaa6ed064a66f4dfd339aab3b052a335c7c7073403a1
26,498
null
"\"\"\"T-composed categorical-digit reasoner with fully learned pair/reduction maps.\"\"\"\n\nfrom _(...TRUNCATED)
"{\n \"id\": \"001e4315-d76b-43da-8a88-0f8a9d2e3f95\",\n \"created_at\": \"2026-08-04 15:19:10.646(...TRUNCATED)
"{\n \"score\": {\n \"mean_loss\": 2.206954932994406,\n \"primary_metric\": \"mean_exact_accu(...TRUNCATED)
00224bd0-73fb-4a40-97f8-a9b07f9da26e
easy
shreyash-chonkie
2026-08-26 14:24:03.669929+00:00
succeeded
c55d3c9a0c2334b02bb81847ede9332c2e7c70fa8081fb22b066ce23f236b91d
11,398
null
"\"\"\"Ternary register machine with supervised execution prefixes.\"\"\"\n\nfrom __future__ import (...TRUNCATED)
"{\n \"id\": \"00224bd0-73fb-4a40-97f8-a9b07f9da26e\",\n \"created_at\": \"2026-08-26 14:24:03.669(...TRUNCATED)
"{\n \"score\": {\n \"mean_loss\": 1.944562554359436,\n \"primary_metric\": \"mean_exact_accu(...TRUNCATED)
0029af78-e042-41d6-8ff6-f63b473acf21
easy
erdavis0
2026-08-22 07:43:23.646040+00:00
succeeded
a1c22e3f3f7475c81762f81ac0379de4cfc7d9c3c89e6889339eb862577f0cc4
6,485
null
"\"\"\"Field crossbar with learned row and column summaries.\"\"\"\n\nfrom __future__ import annotat(...TRUNCATED)
"{\n \"id\": \"0029af78-e042-41d6-8ff6-f63b473acf21\",\n \"created_at\": \"2026-08-22 07:43:23.646(...TRUNCATED)
"{\n \"score\": {\n \"mean_loss\": 6.961434841156006,\n \"primary_metric\": \"mean_exact_accu(...TRUNCATED)
End of preview. Expand in Data Studio

One Layer Deeper submissions

This dataset archives 15,602 accepted uploads from 206 GitHub accounts to the One Layer Deeper competition. It contains 9,627 distinct source files, all upload metadata, and stored evaluation results. Snapshot: September 7, 2026, 21:48 UTC, after the August 31 submission deadline.

Split Uploads Succeeded Failed
easy 11,961 11,112 849
medium 2,704 2,509 195
hard 937 847 90

All accepted uploads are included: practice runs, failures, identical resubmissions, and superseded or excluded entries. “Succeeded” is the evaluator's run status, not a claim of task correctness or rule compliance. GitHub accounts do not necessarily represent distinct people. Rejected requests and local experiments are outside this archive.

Load the data

This is a private dataset under GPUMODE. Access requires an authorized Hugging Face account. Run hf auth login first; datasets uses that local authentication for the download.

from datasets import load_dataset

hard = load_dataset("GPUMODE/one-layer-deeper-submissions", split="hard")
row = hard[0]
print(row["id"], row["github_login"], row["status"])
print(row["source"][:500])

Install the datasets package to use this example. Loading reads participant source as text; executing that source is unnecessary for archive analysis.

Each row has these fields:

Field Meaning
id Accepted submission UUID
tier easy, medium, or hard
github_login Submitting account label
created_at Stored upload timestamp, including UTC offset
status Stored evaluator run status
sha256 SHA-256 of the original UTF-8 source bytes
source_bytes Original source length in bytes
leaderboard_rank Rank of this exact upload in the archived public leaderboard, or null
source Complete original submission.py decoded as UTF-8, preserving line endings
metadata_json Complete original metadata.json file as a UTF-8 string, preserving formatting
result_json Complete original result.json file as a UTF-8 string, preserving formatting

A failed run may have JSON null in result_json; use json.loads(row["result_json"]) to interpret it. Scores are recorded evaluator outcomes, not independent reproductions. A high score does not establish rule compliance or generalization beyond the recorded tests.

Original files and integrity

data/{easy,medium,hard}.parquet   One row per accepted upload
archives/{easy,medium,hard}.tar.gz
  submissions/<tier>/<id>/submission.py
  submissions/<tier>/<id>/metadata.json
  submissions/<tier>/<id>/result.json
inventory/manifest.json
inventory/export_summary.json
inventory/statistics.json
inventory/engagement.json
inventory/leaderboard.json
verification.json               Local packaging verification report
checksums.json                  File SHA-256 hashes and sizes
SHA256SUMS                      Checksums of every other packaged file

The tar archives preserve all three files byte for byte. Archive order and timestamps are normalized; the files' content bytes are unchanged. Parquet strings also round-trip exactly to the original bytes via UTF-8 encoding, including CRLF line endings and JSON whitespace.

import hashlib
import json

source_bytes = row["source"].encode("utf-8")
assert len(source_bytes) == row["source_bytes"]
assert hashlib.sha256(source_bytes).hexdigest() == row["sha256"]
metadata = json.loads(row["metadata_json"])
result = json.loads(row["result_json"])

Packaging verified every Parquet source hash, every metadata/result string against its original bytes, every archive entry against its original file, and exact submission-ID coverage in each split. checksums.json maps every substantive file path to its SHA-256 and byte length, excluding the two checksum manifests. SHA256SUMS additionally covers checksums.json; it does not hash itself. With the repository downloaded locally, run sha256sum -c SHA256SUMS (or shasum -a 256 -c SHA256SUMS on macOS).

Provenance and scope

The original export used a read-only, repeatable-read transaction against the organizer database. Every source matched the database's md5(source) value, and SHA-256 was saved per upload. The public leaderboard was fetched separately immediately before that transaction. inventory/export_summary.json records the snapshot and exclusions; inventory/manifest.json retains per-upload provenance.

This release explicitly includes only the submission files, five listed inventory files, and packaging documentation. It does not include dataset inputs, trained checkpoints, raw service logs, metric histories, moderation records, credentials, or email fields. Participant source is retained as submitted. Moderation status is not a row-level classification in this release, and absence from the leaderboard alone does not establish a reason for exclusion.

Participant files retain their existing ownership and terms. No blanket license or relicensing is asserted for these uploads. The upstream service's Apache license does not automatically apply to participant submissions.

The packaging script reads code as bytes and text and never imports or executes participant source. Rebuilding with the same inputs and Python/PyArrow versions produces deterministic archive content and packaging metadata; the exact runtime versions are recorded in verification.json.

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