CogniRank: independent reproduction

This public dataset repository distributes evaluation software, including a single self-contained cognirank.py. No Modal account or original project checkout is needed. The November update adds SmolScript at 20%, using 350 fixed examples by default across nine programming languages. The script embeds the five benchmark implementations, SmolCUA questions and answer keys, the fixed SmolScript subset and execution sandbox, source checksums, and selected Procedural Pile row indices. Internet access is needed for Python packages, model weights, and the other pinned datasets.

The evaluator, source manifest, and published measurements for this update are frozen at dataset commit 5f5969a2bae453e45f2a51c746908e16b21cc2ab. The command below downloads that exact evaluator.

Run

Use Linux, Python 3.11, git, a C/C++ build toolchain, Docker Engine, and an NVIDIA GPU supporting CUDA and bfloat16, with enough GPU memory for your model and the fixed batch sizes. Allow up to 16 GB of host RAM for eight concurrent execution containers. Download cognirank.py from this public repository. Authenticate with Hugging Face only if a selected model requires access:

curl -L https://huggingface.co/datasets/dotlabs/cognirank/resolve/5f5969a2bae453e45f2a51c746908e16b21cc2ab/cognirank.py -o cognirank.py
python3.11 -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python cognirank.py --verify
python cognirank.py --install-deps
python cognirank.py --build-sandbox
python cognirank.py --model dotlabs/void.1 --output-dir void1-results

Multiple models run sequentially:

python cognirank.py --models dotlabs/void.1 MinimaLabs/KLM-1.5 ibm-granite/granite-4.0-h-350m --output-dir comparison
python cognirank.py --models dotlabs/void.1 MinimaLabs/KLM-1.5 ibm-granite/granite-4.0-h-350m --output-dir comparison --resume

--dry-run prints the protocol and model revisions without installing dependencies or running inference. --extract-only ./cognirank-source extracts the complete bundled source for inspection. --print-requirements prints dependencies. Installation explicitly executes pip in the current environment; inference permits Hugging Face custom model code, as the original evaluator does.

Known model revisions are pinned in source_manifest.json. Other model IDs resolve main once and record the immutable revision. To choose a different revision for one model, use --model-revision COMMIT_SHA. Use a new output directory for a different protocol, revision, or context. Resume preserves the resolved revision and verifies completed component checksums; SmolCUA resumes individual predictions, and Procedural Pile can re-score saved generations. Interrupted MARC/TTM components restart. SmolScript reuses a fully saved generation checkpoint and repeats unfinished component scoring.

Exact protocol

Component	Examples	Weight	Metric
SmolCUA v1.4	1,200: three fixed passes of 400	24/95	Mean weighted pass score, 0–100
MARC	320: 4 families × 4 levels × 20	20/95	Mean exact-match accuracy of MARC-0…3
Procedural Pile	Fixed 350 across all 55 task families	20/95	Mean official verifier reward × 100
Tiny Theory of Mind	All 2,000	12/95	Token-normalized likelihood multiple-choice accuracy
SmolScript	Fixed 350 across nine languages	19/95 = 20%	Macro-average of nine language Pass@1 accuracies

CogniScore = (24*SmolCUA + 20*MARC + 20*ProceduralPile + 12*TTM + 19*SmolScript)/95. All components are required. Unsupported context is reported per benchmark; completed component scores are retained, the overall index is omitted, and the model is excluded from the ranking. Other failures still stop the run. The rounded display weights are 25.26%, 21.05%, 21.05%, 12.63%, 20.00%; computation uses the exact fractions above.

Generation is greedy, bfloat16, CUDA device 0. SmolCUA uses automatic chat/plain formatting, batch 32, a 512-token response cap (48 for short formats). MARC uses the upstream plain demonstration prompts, deterministic seed string 0, batch 32, and 200 generated tokens. Procedural Pile uses automatic chat formatting with thinking disabled, batch 8, and 512 generated tokens. TTM uses plain continuations and lm-eval==0.4.12, batch 8. SmolScript uses plain code-completion prompts for base and instruction models alike (Python prefix def solve(n):\n, without trailing indentation), batch 64, one greedy continuation, and language/difficulty budgets of 96–256 tokens. Hidden tests determine correctness by execution or DOM/computed-style assertions. Equivalent behavior passes regardless of code text; whitespace is preserved where it changes program or string semantics. Partial hidden-test pass rate is reported separately and does not replace task Pass@1. Full implementations are embedded; prompt construction, whitespace-aware scoring, and selection indices can be inspected with --extract-only.

Default requested evaluation context is 4,096. Extending a model's declared context is recorded as a context-extension experiment. Known fixed learned-position architectures are capped at their hard positional limit. Response budgets shrink to remaining space; input prompts are never truncated. A prompt that cannot fit produces an explicit unsupported-context report. --eval-context 0 uses the native context, capped at 4,096 by this runner; changing context changes the experiment. Effective limits and shortened response budgets are recorded in component outputs.

Procedural Pile uses the pinned official verifier with inverse-math-normalize-progress-v1: it rejects a non-progressing malformed LaTeX fraction instead of hanging indefinitely. Other verifier timeouts fail the evaluation rather than silently assigning zero. The patch and upstream license are embedded in inverse_math_normalizer.py.

Immutable sources

• SmolCUA: dotlabs/smolcua, revision faf2b33f893573e23666a4e59c99a09d0b73ba51, runs/v1_4_seed_20261023/. Bundled questions SHA256: 74fb0a12a8dd75cbd8675d76e8bcf4a16e1b424668a195f4ff9a31da2becf065.
• MARC generator: FlameF0X/Micro-ARC/marc.py, revision 4ed8c708aa3e9665a9d9c81f2e3bc586cf60e14e.
• Procedural Pile: reasoning-core/procedural-pile, revision 0e3216537b0cb7df8709c6cae34bfceae6450068. Full test shard SHA256: 4ed623b9ad54a2f08483c8281825000e2f46c1e8ccfdeddd1db1cb1ca5c6cac8. The 350 selected IDs and family counts are embedded in source_manifest.json; selection version procedural-pile-350-v1, subset SHA256 67647511ff184a44c9f1cb7abaed66e0cd113f2deecafba95250b9a7338ddf6f. Twenty families contribute seven rows and 35 contribute six; exact hash-based selection is in the bundled parser.
• Official verifier: sileod/reasoning_core, revision 68b0b3a0f29128d2c56b3eafcdaa34777b9e1279.
• TTM: AxiomicLabs/Tiny_Theory_of_Mind, revision ba4c644ea7ace67e096305fe2c9f87c4cf4879ad, SHA256 58827a0361597ae4d5ffa79ddbab0936d1509cafcfc02cbb731e4d7a7f3584b8.
• SmolScript: dotlabs/smolscript, revision fa26615d2eb867def83882444324babb6eb2f4e6; fixed dataset selection smolscript-0.3.0, evaluation protocol smolscript-0.5.0, prompt version smolscript-base-completion-v3. The bundled manifest lists all 350 IDs, language/difficulty allocations, and data/test SHA256 checksums.
• KronLSTM dependency: NX-AI/KronLSTM, installed without dependency replacement after the core requirements.

The source manifest includes all 15 published model repository names and exact revisions, every bundled source checksum, and source download URLs. Save the Hugging Face commit of this release and SHA256SUMS alongside your results.

Execution sandbox

--build-sandbox builds the embedded Dockerfile once with internet access. It uses Playwright Python v1.56.0-noble, Chromium, GCC/G++, Node, Lua 5.4, Mono C#, Rust, html5lib 1.1, and tinycss2 1.4.0. Distribution runtime packages resolve at image build time; keep the recorded image ID or archive the image for byte-identical reruns. Browser/runtime preflight runs before SmolScript generation. Each candidate executes in a fresh credential-free container with network disabled, a read-only root filesystem, an unprivileged UID, dropped capabilities, 2 GB memory, one CPU, a 512-process limit, and phase-specific timeouts. No candidate code executes on the inference host. Infrastructure failures stop evaluation without assigning a zero. References and hidden expectations stay outside candidate containers; only fixtures and input values cross the boundary. Evaluation does not need internet inside the sandbox.

The single-file evaluator needs no Modal account. Its local Docker runner uses the same prompts, generation implementation, hidden tests, source builders, browser assertions, and scoring as the hosted run. Container scheduling and image builds differ; runtime identities and preflight output are recorded.

Outputs and reproducibility limits

Each model directory contains run.json, results.json, per-component logs and predictions, environment.json, and pip-freeze.txt. comparison.json contains the completed model scores. published_results.json records the 15 published checkpoint revisions, component scores, updated indices, and original result checksums. SmolScript also records per-example outcomes, per-language accuracy, syntax success, test pass rate, and its sandbox image identity. The release identifier hashes bundled source files; resume rejects a different release. The single-file archive is checksum-verified before extraction.

This release implements the November CogniRank update. Previous four-component aggregate scores are superseded; unchanged component measurements are reused only at the same model revision and evaluation context. The published snapshot combines those measurements with completed SmolScript 0.5.0 runs, and records the source result checksums. Older SmolScript protocols are excluded. Core inference versions are pinned; some secondary dependency ranges remain flexible and the actual environment is recorded. GPU/kernel differences can affect numerical results. The portable packaging and score recomputation were checked locally. A complete GPU benchmark has not been rerun with this distribution, and the full SmolScript reference suite remains execution-unverified; see its bundled validation report. Published model runs are completed model evaluations, not a claim that every reference solution has been validated.

Source datasets retain their upstream licenses and attribution. SmolCUA's source README and the inverse-math patch's MIT notice are included in the archive.