Datasets:

arpandeepk
/

code-atlas-provenance

Role	Sources	Decision
Base pretraining and repository loss	The Stack v3 TRAIN	Primary source; admit only resolved, allowlisted permissive files in the main condition.
Same-problem auxiliary ablation	Project CodeNet	Eligible only after whole-problem-family splitting and global deduplication.
Task/SFT data	McEval-Instruct; conditional Nebius task/trajectory subsets	Never merge silently into the base-pretraining condition.
Functional evaluation	McEval plus a later untouched temporal/private set	Quarantine complete task families from every training source.
Provenance/contamination firewall	SWE-rebench V2, PRs, leaderboard, and repository pointers	Use repo, commit, PR, patch, tests, and task IDs to exclude overlap.
Metadata-only/legal quarantine	MultiPL-E; CuTe DSL until permission	Publish pointers and exclusion decisions, not training payload.

Nebius does not distribute one reusable full-repository archive per task. Its records contain repository and base-commit pointers, patches, tests, issue text, and execution metadata. Reconstructed repositories are useful for execution and contamination checks; adding them again to Stack v3 would usually duplicate core code.

Provisional matrix panel

The audit panel contains 24 mutually exclusive language buckets: the exact 20 SWE-rebench V2 languages plus Haskell, Fortran, COBOL, and Zig. This creates 576 directed cells (including diagonals) and 276 unordered bilingual pairs. CuTe DSL is a separate Python-embedded adaptation target, not a 25th mutually exclusive file language.

The panel is not final until permissive post-filter tokens, unique repository families, clone concentration, parser/compiler success, and evaluation coverage are measured. See registry/audit_panel.json.

First reproducible measurements

The Stack v3 source is pinned to revision a34523fd426a429b7686a5e5786496bf2e1b1172. The official statistics show why the project must distinguish raw availability from training-eligible availability:

Language	Raw estimated tokens	Permissive-labeled estimated tokens	Permissive share
Python	200.865B	12.586B	6.27%
C++	801.913B	32.642B	4.07%
Rust	19.135B	4.363B	22.80%
Haskell	2.063B	107.9M	5.23%
OCaml	1.343B	57.5M	4.28%
Julia	1.851B	43.7M	2.36%
Elixir	1.148B	159.9M	13.93%
Zig	909M	33.3M	3.67%
Fortran	7.055B	135.9M	1.93%
COBOL	530M	7.1M	1.34%

These are still upper bounds before the project SPDX allowlist, vendor/generated filtering, parser audit, benchmark firewall, repository-family grouping, and global deduplication.

A record-level pilot has now been run on pinned Stack v3 shard part-00000. The 416,084,426-byte Parquet file contains 21,137 repository rows and 241,665 files; every declared file count matched the nested payload. Under the deliberately incomplete pilot gate (one of the 24 panel languages, Stack's permissive label, and non-vendored), 5,054 content IDs survive and none repeats in the shard. Some low-resource languages have no surviving file in this particular shard, so it is strictly a pipeline validation—not a basis for extrapolating final totals. The full 8,192-shard scan and explicit detected-license allowlist remain pending.

Pinned SWE-rebench V2 contains 32,079 tasks across 3,617 repositories and 20 language labels. Its distribution is highly uneven: Python has 7,243 tasks over 691 repos, Go 6,144 over 967, C++ 182 over 17, OCaml 58 over 18, and Lua 39 over seven. It has no Fortran, COBOL, Haskell, or Zig task slice.

Pinned McEval contains exactly 16,031 rows over 40 language labels and six task surfaces. Several completion surfaces are variants of the same canonical problem; they must be grouped before any split. Pinned McEval-Instruct contains 35,943 rows over 36 case-normalized labels, not the roughly 110K examples reported for the original construction. Artifact versions therefore matter.

The pinned SWE-rebench leaderboard payload contains 860 unique instances. Its 15 monthly partitions contain the same 860 instance IDs as the aggregate test partition, so counting every advertised split produces 1,720 rows but no extra tasks. The pinned Parquet schema also lacks the license_name field described by the dataset card. It remains a frozen evaluation/firewall source, never a training source.

The remaining pinned Nebius task artifacts are also locally complete and checksummed. SWE-rebench V2 PRs contains 126,300 rows but only 122,910 unique repo/pull IDs; 1,920 IDs repeat, producing 3,390 surplus rows, and the payload has no language column. The OpenHands release contains 67,074 unique trajectories over 6,306 issues and 1,823 repositories; 3,792 issues have at least one successful trajectory. These are post-training/task artifacts, not additional full-repository pretraining corpora.

The complete Project CodeNet 1.0.0 archive is now pinned by SHA-256. A census of all 4,053 per-problem metadata files reproduces exactly 13,916,868 submissions, 7,460,588 accepted submissions, and 55 mapped language/category labels. It also materializes 55,230 problem-language cells for leakage-safe family selection. Notably, Fortran has 15,059 submissions across 1,088 problems, whereas COBOL has 1,277 across 340; these are useful controlled-transfer examples but tiny compared with C++'s 8,008,527 submissions. The paper confirms CDLA-Permissive-2.0, although the downloaded archive omits a packaged license notice; any derivative release must add the agreement text.

The pinned mHumanEval commit does not contain the advertised public artifact. A complete streaming audit found only 17 partially populated programming languages and 544,911 logical rows, versus the claimed 25-language, 836,400-row grid. Eight claimed languages are wholly absent: Bash, D, Julia, Kotlin, MATLAB, Visual Basic, Fortran, and COBOL. Consequently mHumanEval cannot supply the remembered public COBOL/Fortran evaluation data at this revision. The complete archive hash, per-language census, and every discrepancy are published in the provenance repository.

Exact ATLAS scale constraint

The ATLAS full grid spans 20 reported sizes from 9,044,352 to 8,452,190,208 parameters. Its transfer matrix uses the 2,034,422,912-parameter scale-34 model, 50/50 bilingual sampling, and a 42B-token reference horizon. See docs/atlas_replication.md for every architecture dimension.

For 24 languages there are 276 unordered bilingual runs. Under ATLAS's BTS definition, the zero-transfer point is 2 * 42B = 84B total bilingual tokens; a full direct grid reaching that point would therefore be 23.184T tokens, before monolingual baselines and scale experiments, and negative-interference curves can require more. ATLAS itself measured a subset and estimated the larger matrix using continual-pretraining adaptation signals; Code-ATLAS will label every cell as measured or estimated.

Split lock

Do not create final train/validation/test IDs until all admitted sources have been globally deduplicated. The loss-test target follows ATLAS exactly per language: min(20,000,000 reference tokens, 20% of unique eligible tokens). A new validation target is min(2,000,000, 10%). Repository/clone/problem connected components are indivisible. See docs/split_protocol.md.

Publication

The public release is divided into provenance, permissive payload, parallel auxiliary, and evaluation-manifest repositories. Restricted sources are visible as metadata-only exclusions. The provenance repository is public at https://huggingface.co/datasets/arpandeepk/code-atlas-provenance. The permissive payload repository will not be created until record-level license and contamination gates pass.