Taiga-S1

A 1.2M-parameter model that builds CAD parts in FreeCAD.

Taiga-S1 is an experiment in whether small, fast decision models can be useful for computer-use agents: a planner decides what to do, and a tiny model handles the step-by-step execution. FreeCAD is the testbed. The next step is the same approach for applications without a scripting API, using the operating system's accessibility tree as the interface.

Taiga-S1 is the fast "System 1" layer for a CAD agent. You give it a goal, an ordered list of features like "plate 40×30×10 → Ø6 hole at (10, 0) → polar pattern ×6 → fillet the top edges". It builds the part command by command: select a plane, sketch, draw, constrain, pad, pattern, fillet. At every step it reads FreeCAD's live state (feature tree, selection, sketch constraints, workbench) and picks the next command from the ones currently available.

• Tiny and fast. 1.2M parameters, trained from scratch, ~1 ms per decision on a CPU. No LLM, no vision model, no screenshots.
• Handles longer goals than it trained on. Trained on goals of up to 5 features, it completed all 100 held-out 11-feature goals (~55 commands) and 95% of 17-feature goals.
• Recovers from mistakes. With 20% of its actions replaced by random ones, it notices the damage, undoes it and finishes 86–100% of parts.
• Runs in the real FreeCAD app. It drives the FreeCAD GUI over a local socket and builds parts live.

Results

"Built correctly" means the model finished and the final solid matches the target exactly (volumetric IoU ≥ 0.99, no stray objects). Each row is 100 fresh synthetic goals (same feature vocabulary as training, longer or recombined) in FreeCAD 1.1 (60 per length for 13–17 features). Per-step accuracy against the teacher's choices is 99.8%.

What made it generalize

1. Randomized position IDs during training (Ruoss et al. 2023). Position numbers the model had never seen were what broke it on longer parts.
2. Coupled ordinals. Goal item k and the k-th feature in the tree share an index (position coupling).
3. A modular "done?" policy. Each goal item asks "am I built yet?", and the model acts on the first one that isn't. This is what made the longest goals reliable across training seeds.
4. Factorized feature types. Category embeddings plus type dropout help with feature pairings it hasn't seen.

Usage

from freecad_s1.model.net import from_pretrained
from freecad_s1.rollout import Policy

model = from_pretrained("shhivv/taiga-s1")
policy = Policy(model, device="cpu")

probs = policy.score(state, goal, actions)   # {command: probability}, best first
next_command = next(iter(probs))

What to pass in:

• state: a snapshot of the FreeCAD session, from the included runtime (freecad_s1.runtime).
• goal: the ordered feature list, plus a rough size of the finished part (bounding box, volume). Estimates are fine.
• actions: the commands currently available in FreeCAD, as returned by the runtime's valid_actions().

The returned probabilities are calibrated. The fitted temperature is stored in config.json.

How it was trained

• Data. 24k synthetic modeling sessions scripted in headless FreeCAD, about 590k decisions. A scripted teacher labels the right next command at every step, and random mistakes are mixed in so the model also learns to recover.
• Training. Supervised training, then two rounds of DAgger: the model drives FreeCAD itself and the teacher corrects what it gets wrong.
• Architecture. A 3-layer transformer encodes the session state and the goal; candidate commands attend to the state and the active goal item, and each gets one score.

Scope. It covers FreeCAD PartDesign workflows: sketches (rectangle, circle, hexagon), pad, pocket, hole, revolve, linear and polar patterns, mirror, fillet, chamfer and shell. Taiga-S1 chooses the command; numeric values come from the goal.

Citation

@misc{taiga_s1_2026,
  title  = {Taiga-S1: a small next-action model for CAD},
  author = {Shanmugam, Shiv},
  year   = {2026}
}