cua-s1-forms

A small, jev-like ("System One") one-pass option scorer for GUI form filling, trained to work as the decision layer behind cua-driver.

Unlike an autoregressive LLM, this model does not generate text. Given a UI element and a list of typed options (one option per document entity, plus check / click / skip), it returns one probability per option in a single forward pass — the same input/output contract as TypeSafe's Jev. Every actionable element on a form is scored independently and in parallel in one batch; execution order (fills, then checkboxes, then the one submit click) is decided by downstream code, not the model.

Full writeup, training code, synthetic data generator and live Cua Driver integration: https://github.com/trycua/cua/tree/main/libs/cua-s1.

Architecture

• Byte-level embedding + 2-layer Transformer encoder (width 128, 4 heads) over the context and, separately, over each option's text
• jevlike's AttentionHead: each option becomes a query against the context tokens, producing an attended context vector, then a shared dot product turns each (option, attended-context) pair into one logit; softmax over the live option count
• 706,048 trainable parameters, 2.8 MB checkpoint (state_dict + config + training history + best validation metrics)

Input / output

Context (one per element, byte-truncated to 224 bytes):

TASK fill the form from the document, then submit
FORM Northwind Clinic - New Patient Registration
ELEMENT Edit "Phone number" value=""

Options (one per document entity, plus the three fixed actions, byte-truncated to 96 bytes each): fill Tel: (503) 555-0142, fill DOB: 03/14/1987, ..., check, click, skip.

Output: one probability per option. The executor picks the argmax, looks up the entity by index if the action is fill, and orders the resulting actions before sending them to cua-driver (set_value / click).

Training

• 10,000 synthetic episodes (cua_s1/synth.py): random form (2–16 fields from a 55-concept catalogue with form-label/document-label synonyms), random person, random document with distractor entities and forced look-alike confuser pairs (e.g. email vs street, phone vs emergency contact phone, state vs university), random window-title suffixes and 20% title dropout
• Splits are disjoint by exact form field signature — a test form's field set never appears in training
• AdamW, cosine schedule with warmup, 6 epochs, batch size 128, cross-entropy over the live option count

Results (see the repo's docs/RESULTS.md for the full ladder)

Head-to-head against the real hosted Jev API (jev-latest, zero fine-tuning, same task): 99.7% for this model vs 83.6% for hosted Jev overall; 96% for hosted Jev on decisions that require real judgment (fill vs check vs click) and 74% on recognizing an already-filled field as a no-op — a convention this model was trained on and hosted Jev was not. Full numbers in the repo.

Files

• cua-s1-forms.safetensors + cua-s1-forms.json — the checkpoint in the format cua_s1.checkpoint expects: tensors only in safetensors, everything else (architecture config, a SHA-256 signature over the tensors, free-form metadata) in a plain JSON sidecar. This is the format to use; cua_s1's own loader rejects pickled .pt/.pth files by design (arbitrary pickle is a code-execution risk for a public checkpoint).
• cua-s1-forms.pt — the original PyTorch pickle checkpoint, kept only for anyone still loading it directly with torch.load(..., weights_only=False) outside cua_s1. New code should use the safetensors pair above.

Both encode the exact same weights; converted with a script that reimplements cua_s1.checkpoint.save_checkpoint_files's exact document/signature format, and verified to produce bit-for-bit identical model output against the original .pt.

Usage

from pathlib import Path
from huggingface_hub import hf_hub_download
from cua_s1.model import load_checkpoint, select_device

repo = "cua-ai/cua-s1-forms"
weights = Path(hf_hub_download(repo, "cua-s1-forms.safetensors"))
hf_hub_download(repo, "cua-s1-forms.json", local_dir=weights.parent)  # sits next to the weights

# validates format, version and SHA-256 tensor signature before returning
model, collator, config = load_checkpoint(weights, select_device("auto"))

See cua_s1/planner.py for the full snapshot → score → order → execute loop against a live Cua Driver session.

Limitations

• Only ever chooses among entities a PDF/document extractor already found as Label: value pairs; it cannot invent a value.
• Trained entirely on synthetic forms plus a small (196-decision) real eval; not validated on arbitrary real-world forms outside the demo set.
• Byte-level encoder, English-centric label vocabulary.
• Not calibrated with TypeSafe's RLCD method — this is an independent research checkpoint, not a reproduction of Jev.

License

MIT.