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Frequently Asked Questions

Is GenoLeWM a clinical tool?

No. GenoLeWM is alpha research software. No corrected scientific score is currently published, and no output is a clinical diagnosis, clinical risk probability, or medical advice.

What does the model do?

GenoLeWM treats a genomic edit as an action. Carbon encodes a reference DNA window into a latent state, the action encoder embeds the edit, and the predictor estimates the post-edit latent state.

What is Carbon's role?

Carbon-500M is the frozen state encoder in the released path. GenoLeWM does not retrain Carbon. The trainable components are the action encoder and predictor. The released wrapper executed Carbon's custom tokenizer, whose pinned source made an unpinned, network-capable Qwen/Qwen3-4B-Base lookup. The corrected source path uses a self-contained pure-DNA tokenizer built from the mounted Carbon files instead. That repair is not a corrected model result.

What does sigma_raw mean?

sigma_raw is the uncalibrated distance between the predicted post-edit state and the Carbon-encoded edited state. That definition is meaningful only when both states share a declared, validated contract. Published checkpoints use legacy_raw_v1, which mixed raw targets with unit-normalized predictions; training sources and targets also used global versus edit-centered pooling, and every historical centered pool was shifted one hidden token left by a missing <dna> control-token offset. Their sigma_raw values are invalid as edit-effect or surprise scores. They are retained only for historical compatibility and are not probabilities of pathogenicity.

What does sigma_calibrated mean?

sigma_calibrated maps sigma_raw through a calibration table. The released table was fitted to invalid mixed-contract residuals, so published calibrated values are historical outputs, not contextual scientific scores. A future l2_normalized_v2 lineage needs a new calibration table and fresh validation.

What does a checksum receipt verify?

A receipt binds model manifest identity, optional input commitment, and output commitment. It supports reproducible artifact inspection and tamper detection. It does not prove model quality, clinical validity, privacy, or runtime behavior. In particular, hashing the historical Carbon tokenizer.py did not bind the mutable tokenizer it fetched transitively from Qwen/Qwen3-4B-Base.

Is Carbon tokenization self-contained now?

The current source path used for new l2_normalized_v2 work implements the pure-DNA branch locally from Carbon's pinned DNA and tokenizer configuration. It does not execute the upstream network-capable tokenizer wrapper. It also validates the <dna>/</dna> layout and resolves the edit center after the leading control token. These are fail-closed runtime invariants; no fresh checkpoint has yet established model quality under them.

Can I score a VCF?

Yes, with local model artifacts and a local FASTA reference:

geno-lewm-score \
  --model-dir /path/to/model \
  --backend auto \
  --vcf variants.vcf \
  --fasta reference.fa.gz \
  --output scores.jsonl \
  --receipt receipts.jsonl

The scorer requires the reference alleles in the VCF to match the FASTA windows it extracts.

Can I run it in the browser?

The Hugging Face Space is a public artifact console: https://huggingface.co/spaces/abdelstark/geno-lewm. It can inspect artifacts and checkpoint metadata. Legacy scientific scoring is disabled. Do not use it for private genome data.

Does GenoLeWM beat Carbon?

The published v0.2.1 comparison cannot answer this question. That run used legacy_raw_v1; its residual-based VEP values are invalid, and its cosine values are confounded by mixed-scale, mixed-coordinate training and a changed rollout source representation. The exact rows and signed deltas remain available for artifact audit, but they support neither a superiority nor an inferiority claim about l2_normalized_v2. No corrected comparison is currently published.

What does the planning demo prove?

It proves that the released manifest-backed legacy path executed against public artifacts. Its L2 objective compared incompatible state scales, so the operands also lacked a consistently committed pooling center. The reported objective is invalid and the demo does not prove useful edit selection or biological design capability.

Does the model support structural variants?

The released scoring and training surfaces focus on short variants. Large structural variant support is not established by the public release.

Does the model keep my data private?

The current source runtime is local-first: local VCF/FASTA inputs, local model artifacts, local output paths, no telemetry by default, and redacted logging. The published lineage predated the self-contained tokenizer repair and could make the unpinned Qwen tokenizer lookup described above. Local-first is a design and implementation boundary, not a general privacy certification.

Where are the public artifacts?

How do I contribute?

Open a focused issue or pull request with tests and validation. Public docs should stay tied to measured artifacts, explicit state contracts, and current validity boundaries.