ABLE: Active Boolean Learning Engine

Model weights accompanying the paper "ABLE: Choosing Perturbation Experiments to Recover Gene Logic" (AI for Science Workshop at ICML 2026).

ABLE is a neuro-symbolic pipeline for recovering executable Boolean regulatory rules from perturbation-state transition data, with support-conditional uniqueness certificates and active experiment planning. This repo hosts the paper's released checkpoints. The public code lives in a companion package (able-public); see the reproducibility README there for install and reproduction commands.

Contents

File	Size (bytes)	SHA-256	Purpose
checkpoint_n50_ncf_best.pt	24,097,458	57c968490a2f1535582cc009fc38f659b6fe4b56f89bf72c9bcfb285640a0c8d	Main 50-variable NCF-pointer proposer. Used for BBM (Table 2, Figs. 2/3/4/6), Ablation A (Table 9 row), and all default evaluation commands in the public README.
checkpoint_n15_ncf_best.pt	23,965,466	26cdef1bb4bfb39fbb4c278d2f40528c1328664a80c22c97ee99a901fe4a34f0	15-variable NCF-pointer proposer used for Table 1 (four curated biological networks).
checkpoint_n50_unconstrained_best.pt	25,312,058	03510ef826edce9a53cfa87049abf77cd17ea564e87ef4f06167d19e5b952f83	Ablation B: 50-variable NCF-free decoder variant (unconstrained truth-table head), used only for Appendix Table 9 / Ablation B. See provenance note below.

All three are plain PyTorch state dicts saved via torch.save({"model_state_dict": ..., "optimizer_state_dict": ..., "config": ..., "step": ..., "best_metric": ...}, path); load them with torch.load(path, map_location=..., weights_only=False).

Training recipe (reference)

• Synthetic streaming dataset of k-junta Boolean networks (see NCFStreamingDataset in the paper codebase).
• Transformer backbone: d_model=256, n_heads=8, 4 encoder + 2 decoder layers, pointer dim 64.
• num_steps=300000, AdamW with lr=1e-4, weight_decay=1e-5.
• n=50 runs: num_obs=200, noise_rate=0.05, mixture noise schedule, batch_size=16.
• n=15 run: num_obs=60, batch_size=64.
• Seed 42; single-GPU training.

Exact configs are embedded in each .pt under the "config" key, and are also committed alongside the public training scripts.

Provenance note for checkpoint_n50_unconstrained_best.pt

The original post-paper checkpoint for the Ablation B (unconstrained) variant was unrecoverable at release time. The file in this repo is a retrain produced from the same committed training script and configuration (seed 42, same DEFAULT_CONFIG). It reproduces the paper's expected ablation regime on the synthetic held-out eval (transition_acc bouncing in [0.014, 0.022], tt_bit_acc ~= 0.836, regulator_set_f1 ~= 0.60, functional_agreement ~= 0.92) but will not be byte-identical to the artifact that originally produced the paper's Appendix Table 9 / Ablation B numbers, because synthetic data streaming is sensitive to dataloader-order PRNG draws. Downstream BBM Lift-Cert numbers are expected to be statistically equivalent but may differ within run-to-run noise. If bit-exact reproduction of the paper table is required, rerun the Lift-Cert pipeline against this checkpoint and report the refreshed numbers.

Intended use

• Reproduction of the ICML-2026 AI4Science paper numbers. The companion CLI able-download-checkpoints consumes this repo.
• Research extensions on k-junta Boolean-network recovery from perturbation transitions (neuro-symbolic, active-learning, and certificate-style work).

Limitations

• Trained on synthetic Boolean networks matched to the paper's structural priors (max-indegree 6, mean-indegree ~2.5, NCF-majority distributional prior). Out-of-distribution biological networks may require retraining or domain adaptation.
• Ablation-B checkpoint (*_unconstrained_*) is only meaningful as a control: it removes the NCF prior from the decoder head. It is not the recommended proposer for downstream work.
• The decoder consumes quantised occupancy statistics, not raw state trajectories; inference pipelines must feed data through the paired preprocessing code in able-public.

Download

The companion code package is available at https://github.com/phuayj/able. Install it and run the bundled checkpoint downloader:

git clone https://github.com/phuayj/able.git
cd able
pip install -e .
able-download-checkpoints --output-dir checkpoints

This places all three checkpoint files under checkpoints/. No authentication is required for downloads.

Citation

See CITATION.cff in the paper codebase.

License

MIT (weights released alongside the paper code).