NorthCUA-Pro

Computer-use agents trained through experience at scale

Model family · Performance · Training & report · Quickstart · Citation

Explore the NorthCUA model family

This is NorthCUA-Pro. Looking for another model size? Explore the other two members of our family:

NorthCUA-Nano — 9B total · 9B active · Dense

NorthCUA-Mini — 35B total · 3B active · MoE

Meet NorthCUA-Pro

NorthCUA-Pro is a 397B-parameter computer-use model with 17B active parameters, built on Qwen3.5. As the family's largest mixture-of-experts model, it takes user requests, screenshots, and interaction history and generates reasoning, responses, and tool calls for a desktop or browser runtime.

Required for computer use: Run NorthCUA with our official NorthCUA Harness. The model and Harness are designed to be used together for prompts, tool calls, observations, and action execution.

Strict coordinate contract — normalized_1000: All GUI action coordinates use the 0–1000 normalized coordinate space, not raw pixel coordinates. Keep this convention throughout the model–Harness interaction; the Harness converts coordinates to pixels at execution time.

NorthCUA connects sustained interaction, verified experience, and staged learning to carry tasks across changing software states—from understanding an instruction to producing and checking a result.

• See and act. Visual grounding and multimodal observations support desktop applications and web workflows.
• Learn from complete work. Long trajectories teach dependent steps, intermediate artifacts, and outcome verification.
• Build on open weights. The Nano, Mini, and Pro family spans 9B / 35B / 397B total parameters, released under Apache-2.0.

Model family

Performance

Results reported in the NorthCUA technical report. Grounding uses SFT checkpoints; task completion uses subsequent joint desktop–web RL checkpoints. These research results have not been tied to an exact checkpoint975 export and are not new measurements of the files in this repository.

Sources: Table 6 and Tables 12–13 of the 121-page report. ScreenSpot-Pro uses 1,581 examples, one prediction per screenshot, and no iterative zoom/crop search. OSWorld-G uses 564 refined instructions and the best of three complete runs, not a mean or itemwise pass@3 (temperature 0.7, top-p 0.95, top-k 40, 1,024 output tokens, normalized 0–1000 coordinates).

OSWorld-Verified uses 361 tasks, excluding eight Google Drive tasks requiring separate account configuration. WebArena-Verified uses 812 tasks and official outcome checks. Both have a 100-step task limit. Retained-attempt selection and other protocol details are in §6.1 and Appendix I; compare results with those conditions in view.

The report also lists 66.80% mean partial credit for Pro after RL on OSWorld 2.0 (108 tasks, 500-step limit). This is not a full-completion rate; Nano and Mini results are unavailable in that table. The detailed WebTestBench table marks NorthCUA results as pending, so no score is asserted here.

Training and report

The NorthCUA experience loop — original Figure 2 from the technical report.

1. Harness: a common runtime sustains desktop and web interaction, collects trajectories, and retains workspaces for outcome inspection. Visual context, compaction, and task memory support long workflows.
2. Experience Flywheel: intents, environments, and verifiers become executable tasks. Assessed trajectories provide training experience; the report also proposes skill-guided and checkpoint-assisted collection for hard tasks.
3. Staged Learning: broad GUI mid-training, verified long-horizon SFT, and joint desktop–web GRPO turn complementary experience into an improved policy.

These are rounded corpus/prepared-pool summaries from Table 3 and §5.1, not per-model consumption counts. The research pipeline contains multiple checkpoints and stages.

Figure 2 notes: GUI states, cursor cues, and rewards are schematic. Trajectory rows omit intermediate actions; GRPO retains valid failures alongside successes. The hard-task panel denotes the proposed skill-guided and checkpoint-assisted collection in §4.3. Reusing experience during training does not mean the serving model updates its weights while handling a request.

Quickstart

Required runtime and coordinate co