GenoLeWM

Action-conditioned latent world models for genomic edits.

GenoLeWM models a genomic edit as an action in a latent state space. A frozen DNA encoder maps a reference sequence window to s_t; a trainable action encoder and predictor estimate the post-edit state s_{t+1}.

reference window --Carbon encoder--> s_t
edit spec --------action encoder----> a_t
(s_t, a_t) -------predictor---------> s_hat_{t+1}
edited window ----Carbon encoder----> s_{t+1}
loss = distance(s_hat_{t+1}, s_{t+1}) + collapse regularization

The package is an alpha research system. It is not a diagnostic device, not a clinical decision system, and not evidence of privacy or runtime assurance beyond local execution contracts and checksum provenance.

Install

python -m pip install geno-lewm

Use extras for heavyweight paths:

python -m pip install "geno-lewm[eval]"    # FASTA/VCF evaluation utilities
python -m pip install "geno-lewm[train]"   # torch/transformers training paths
python -m pip install "geno-lewm[dev]"     # tests, lint, typing, packaging

Source development:

git clone https://github.com/AbdelStark/GenoLeWM.git
cd GenoLeWM
uv pip install -e ".[dev,docs]"

What Ships

Surface	Status
Python package	geno-lewm==0.2.1 on PyPI
Core data model	EditSpec, RelEdit, edit application, typed errors, metrics, redaction-safe logs
Model code	action encoder, cross-attention predictor, AR rollout wrapper, CEM planner
Data pipeline	local gnomAD/ClinVar VCF-to-Parquet builders, tuple builder, holdout filtering, dataset packaging
Training	fixture smoke trainer, Carbon preflight, single-process Carbon trainer, packaged run evidence
Evaluation	scorer, Carbon baseline scorer, binary metrics, Spearman metrics, rollout-fidelity metrics, eval aggregation
Demo	terminal scoring demo, checksum receipts, runtime preflight, batch receipt report, planning demo
Release tooling	dataset/model/training/paper package validators, sdist inventory gate, clean-machine replay helpers

Public Artifacts

Artifact	Link
PyPI package	https://pypi.org/project/geno-lewm/0.2.1/
Source/wheel release	https://github.com/AbdelStark/GenoLeWM/releases/tag/v0.2.1
Model package	https://huggingface.co/abdelstark/geno-lewm
Dataset package	https://huggingface.co/datasets/abdelstark/geno-lewm-data
Planning demo artifacts	https://huggingface.co/abdelstark/geno-lewm-runs/tree/main/geno-lewm-v021-strong-4f36eef-10k-r1/planning-demo
v0.2.1 benchmark/planning/paper tree	https://huggingface.co/abdelstark/geno-lewm-runs/tree/main/geno-lewm-v021-strong-4f36eef-10k-r1
v0.2.1 benchmark readiness report	https://huggingface.co/abdelstark/geno-lewm-runs/resolve/main/geno-lewm-v021-strong-4f36eef-10k-r1/suite/model/v0.2_benchmark_readiness_report.json
v0.2.1 generated paper	https://huggingface.co/abdelstark/geno-lewm-runs/resolve/main/geno-lewm-v021-strong-4f36eef-10k-r1/paper/paper.serious-completion.md

Results

The current evidence is useful systems evidence with mixed or negative model-quality results. Do not cite it as broad superiority over Carbon.

Track	GenoLeWM result	Baseline comparison
ClinVar coding	AUROC 0.734375, AP 0.8529761904761904, balanced accuracy 0.75	vs Carbon: AUROC -0.1875, AP -0.09894688644688643, balanced accuracy +0.0625
ClinVar non-coding	AUROC 0.5625, AP 0.6054563492063492, balanced accuracy 0.4375	vs Carbon: AUROC -0.3125, AP -0.30896672771672784, balanced accuracy -0.25
BRCA2 saturation	Spearman rho 0.14919354838709678	vs Carbon: -0.32771260997067453
TraitGym Mendelian	Spearman rho -0.02796450759873114	vs Carbon: +0.05592901519746229
Phased-haplotype rollout	cosine mean 0.28886058350550603, L2 mean 33.319687258878126, Recall@4 1.0	weak vs source-state baseline: cosine -0.7089701215468133, L2 +31.19289051130368
Synthetic edit-chain rollout	cosine mean 0.30160847029349436, L2 mean 28.802888778495763, Recall@4 1.0	weak vs source-state baseline: cosine -0.6896310938123016, L2 +25.637059814259455
AR rollout speed	K=5 speedup 2.413859489667916; K=20 speedup 2.4732225135799566	K=5 passes its local target; K=20 remains below the RFC-0004 5x target
Planning demo	best_distance=23.656930390534644, 384 evaluations, patience stop	released-artifact execution evidence, not useful-planning evidence

The generated paper and readiness report preserve these negative findings. The strongest current claim is that the repository can train, package, evaluate, benchmark, and replay a genomic-edit world-model pipeline with content-addressed evidence.

Quickstart

from geno_lewm import EditSpec, EditType, RelEdit, apply_edits

edit = EditSpec(chrom="chr17", pos=43_091_983, ref="A", alt="T")
assert edit.edit_type is EditType.SNV

rel = edit.relative_to(window_start_bp=43_091_900, window_end_bp=43_092_100)
print(rel.rel_pos)

window = "ACGT" * 64
edited = apply_edits(
    window,
    [
        RelEdit(rel_pos=0, edit_type=EditType.SNV, ref_bases="A", alt_bases="T"),
        RelEdit(rel_pos=4, edit_type=EditType.SNV, ref_bases="A", alt_bases="C"),
    ],
)
print(edited[:12])

Verify a checksum receipt:

geno-lewm-verify examples/data/verify_receipt/receipt.json \
  --manifest examples/data/verify_receipt/manifest.json

Run a local fixture trainer smoke test:

geno-lewm-train --fixture-smoke --run-dir /tmp/geno-lewm-smoke --steps 50

Score a local VCF with a local model package:

geno-lewm-score \
  --model-dir /path/to/model \
  --backend auto \
  --vcf variants.vcf \
  --fasta reference.fa.gz \
  --output scores.jsonl \
  --receipt receipts.jsonl \
  --batch-size 64 \
  --no-progress

Run manifest-backed planning:

geno-lewm-plan \
  --model-dir /path/to/model \
  --window-fasta window.fa \
  --target-fasta target.fa \
  --output plan.json \
  --horizon 5 \
  --iterations 5 \
  --samples 1024 \
  --elite 64

Training Pipeline

The real training path is explicit about data identity, Carbon runtime readiness, config identity, and packaged run artifacts.

1. Validate the checked dataset rebuild spec.

   python -m tools.release.dataset_snapshot \
     --spec-json configs/first_experiment/dataset-snapshot-snv.json \
     --check-spec

2. Check staged upstream inputs before building a dataset snapshot.

   python -m tools.release.dataset_snapshot \
     --spec-json configs/first_experiment/dataset-snapshot-snv.json \
     --check-inputs

3. Run Carbon preflight for the packaged dataset and training config.

   geno-lewm-train --carbon-preflight \
     --dataset-dir /path/to/dataset-package \
     --carbon-model-dir /path/to/carbon-model \
     --training-config configs/first_experiment/train-carbon-500m-snv.yaml \
     --run-dir /path/to/run \
     --preflight-output /path/to/run/training_preflight_report.json

4. Launch training and package the run evidence.

   geno-lewm-train --carbon-train --package-release-run \
     --dataset-dir /path/to/dataset-package \
     --carbon-model-dir /path/to/carbon-model \
     --training-config configs/first_experiment/train-carbon-500m-snv.yaml \
     --run-dir /path/to/run

The run package contains the checkpoint, metrics, logs, resolved config, preflight report, manifest/card, and checksum inventory. Resume checks validate run id, dataset snapshot, seed split, and config identity.

Evaluation And Benchmarks

Compute measured binary or Spearman metrics:

geno-lewm-eval \
  --scores-jsonl scores.jsonl \
  --labels-jsonl labels.jsonl \
  --baseline-scores-jsonl carbon_scores.jsonl \
  --baseline-score-field carbon_zero_shot_score \
  --baseline-name carbon_zero_shot \
  --output-metrics eval_metrics.json \
  --split clinvar_coding

Aggregate metrics and render the release report:

geno-lewm-eval-all \
  --metrics-json eval_metrics.json \
  --output-metrics aggregate/eval_metrics.json \
  --output-report aggregate/eval_report.md \
  --require-v02-vep-metrics \
  --require-v02-rollout-metrics

Run the benchmark-suite command graph from a manifest:

python -m tools.release.v02_benchmark_suite \
  --manifest configs/first_experiment/v0.2_benchmark_suite.template.json \
  --output-report /path/to/v0.2_benchmark_suite_report.json \
  --execute

Measure release efficiency:

python -m bench.inference --release-efficiency \
  --model-dir /path/to/model \
  --variant chr17:43091983:A:T \
  --window /path/to/window.txt \
  --output /path/to/efficiency_report.json

Pure solver timing for planning:

python -m bench.planning --output /path/to/planning.performance.json

Demo Pipeline

Generate a terminal demo transcript from released model/data artifacts:

python tools/demo/terminal_inference.py \
  --model-dir /path/to/model \
  --vcf demo.vcf \
  --fasta reference.fa.gz \
  --output-dir /path/to/demo \
  --backend auto \
  --batch-size 64

The demo writes terminal-demo-transcript.md, terminal_demo_manifest.json, runtime_preflight_report.json, batch_receipt_report.json, scores.jsonl, and receipts.jsonl. These files are release evidence only when generated from non-fixture artifacts and checked by the package validators.

Repository Layout

geno_lewm/      package code
bench/          inference, rollout, training, and planning benchmarks
configs/        checked dataset, training, eval, and benchmark configs
docs/           public documentation and generated API pages
examples/       small public examples and receipt fixtures
rfcs/           design records for stable contracts
tests/          unit, integration, eval, ML smoke, API, and lint gates
tools/          release, CI, dataset, demo, and documentation tooling

Quality Gates

Run the full local gate before publishing code:

uv lock --check
uv run ruff check .
uv run ruff format --check .
uv run mypy geno_lewm tools
uv run python tools/api/snapshot.py check
uv run python -m tools.lint.check_scope_language
uv run pytest
uv run mkdocs build --strict

Package gate:

rm -rf dist
uv run python -m build --outdir dist
uv run twine check --strict dist/*
uv run python -m tools.release.check_sdist_assets dist/*.tar.gz

CI runs the same core contracts plus fixture-backed eval and ML smoke jobs. Fixture outputs are test evidence, not model results.

Limitations

• Current measured benchmarks are mixed or negative relative to Carbon.
• The K=20 AR rollout speed target remains open.
• The released planning demo exercises the model path but does not show useful planning behavior.
• No clinical utility claim; the public model evidence is not clinical evidence.
• Personal-genome workflows are local-first, but local execution is not the same thing as a privacy assurance.
• Checksum receipts prove artifact and output identity; they do not certify runtime behavior.