ti_1k

Screen-grounding training cell from the gui-agent-research pipeline (datagen v2 text negatives + image-neg synthetic screenshots), packed with every screenshot it references so it trains without the AgentNet image archives.

rows	steps	trajectories	text rows	image rows	ref rows	images	size
4148	3037	868	2074	2074	4148	8878	5.6 GB

Systems: {'Windows': 949, 'Darwin': 858, 'Ubuntu': 2341}. Negative types: {'N-stale': 992, 'I-D': 1863, 'N-linger': 1045, 'N-anchor': 34, 'I-D0': 211, 'N-act': 3}. State-line format: list.

Which cell to train on (read this first)

This dataset holds two renderings of the same 4,148 rows (same keys, order, steps, negatives and images). Each belongs to exactly one base model. Never mix them, and never reuse a reference (ref) file across models.

base model	train on	reference log-probs	NOT this
Qwen/Qwen3.5-9B	cells/ti_1k.jsonl	cells/ti_1k.ref.jsonl (shipped, Qwen3.5-9B)	ti_1k_venus.jsonl
inclusionAI/UI-Venus-2-9B	cells/ti_1k_venus.jsonl	not shipped: compute with UI-Venus-2-9B on ti_1k_venus.jsonl	ti_1k.jsonl, ti_1k.ref.jsonl

• Feeding ti_1k.jsonl to UI-Venus-2, or ti_1k_venus.jsonl to Qwen3.5, trains the model in a format it is not evaluated in. Don't.
• cells/ti_1k.ref.jsonl holds Qwen3.5-9B log-probs; using it as the UI-Venus-2 reference is silently wrong. For Venus compute the ref under UI-Venus-2-9B (training-handoff: scripts/ref.sh --model venus; research repo: datagen/scripts/ref.py --backend venus).
• ti_1k_venus.jsonl was added on 2026-11-06 (builder commit 0e1da0e, the same build as ti_5k_venus.jsonl); cells/ti_1k_venus.stats.json has its sha256, builder commit and format pins. ti_1k.jsonl, ti_1k.ref.jsonl and the images are unchanged.

Files

• cells/ti_1k.jsonl -- Qwen3.5-9B only. One row per (step, negative): key, tid, t, system, instruction, images (the step's screenshot window, paths relative to this dataset), messages (chat history up to the step), pos / neg (the two assistant chains; a text negative differs in the <current_state> line, an image negative has the same chain and images_neg = the window with its last screenshot replaced by the synthetic o^-), type, pivot, fmt.
• cells/ti_1k.ref.jsonl -- the same rows with ref_pos / ref_neg / n_pos / n_neg / prompt_tok: sequence log-probs of the stock policy Qwen/Qwen3.5-9B (its chat template, the qwen_vendor message format and image preprocessing) used as the DPO / contrastive reference. These numbers are valid only when that exact model is the reference. Training another base model: keep cells/ti_1k.jsonl and the images, recompute the ref with datagen/scripts/ref.py --chains cells/ti_1k.jsonl --out <ref> under that model, then train. (For UI-Venus-2 do not use ti_1k.jsonl at all: train on cells/ti_1k_venus.jsonl.)
• cells/ti_1k_venus.jsonl -- UI-Venus-2-9B only. The same rows in the official UI-Venus-2 Computer format (github inclusionAI/UI-Venus @ 6d5f8ba, models/computer/computer_example.py, n_img 2, reasoning on): messages = system prompt with the task, each prior step as user ("" or "History Screenshot:" + image) + assistant <think>{state block + action prose}</think>\n<action>{Venus action}</action>, then "Current Screenshot:" + image; pos / neg chain = the continuation after the generation prompt <think>\n, i.e. {thought}\n</think>\n\n<action>...</action>. Actions map 1:1 from the AgentNet ground-truth code (Click(box=(x, y)), Type(content='...\n'), Drag, Swipe, Hotkey, ...). The thought carries the same state claims as ti_1k.jsonl, so the contrast (which claim is wrong) is identical. Same images / images_neg paths.
• cells/ti_1k_venus.stats.json -- counts, sha256, model / vendor / builder pins of the Venus cell.
• images/ -- AgentNet frames (<uuid>.png) and synthetic negatives (<tid>_<t>_<...>.png).
• stats.json -- counts and the sha256 of the source cell.

Layout (setup without the gui-agent-research repo)

pip install -U huggingface_hub
python -c "from huggingface_hub import snapshot_download as d; d('yanghs1018/ti_1k', repo_type='dataset', local_dir='ti_1k')"

Result -- everything lives under one directory, images in a single flat folder:

ti_1k/
  README.md  stats.json
  cells/ti_1k.jsonl         one row per (step, negative)
  cells/ti_1k.ref.jsonl     reference log-probs (Qwen3.5-9B), same rows
  cells/ti_1k_venus.jsonl   UI-Venus-2-9B rendering of the same rows (no ref shipped)
  images/<uuid>.png          AgentNet frames
  images/<tid>_<t>_<...>.png synthetic image negatives (o^-)      (8878 files in images/)

Every image path inside the cells (images, images_neg, synth.neg_png) is relative to the dataset root, e.g. images/9c5b1bde-....png. Resolve it as os.path.join(<root>, path) (or run from <root>); no other path rewriting is needed. hf_pack.py fetch (below) does the same download/unpack and additionally rewrites the paths to absolute ones for the trainer. Disk: about 6 GB.

import json, os
root = "ti_1k"
for line in open(os.path.join(root, "cells", "ti_1k.jsonl")):
    r = json.loads(line)
    pos_imgs = [os.path.join(root, p) for p in r["images"]]
    neg_imgs = [os.path.join(root, p) for p in r.get("images_neg") or r["images"]]   # text negatives reuse the window

Train

python research/screen-grounding/datagen/scripts/hf_pack.py fetch --repo <user>/ti_1k --out <dir>
bash research/screen-grounding/contrastive-dpo/scripts/train_cell.sh --exp <dir> --cell ti_1k --name <run> --loss pairsum

fetch rewrites the image paths to absolute ones under <dir>/images; the trainer reads cells/ti_1k.ref.jsonl. This command is the Qwen3.5-9B path. For UI-Venus-2-9B use the training-handoff repo's venus branch: prepare_data.py --repo yanghs1018/ti_1k, then ref.sh/train.sh --model venus (reads cells/ti_1k_venus.jsonl, computes the Venus ref itself). To get the plain state-line format: refmt.py --cell cells/ti_1k.jsonl --out <x> --fmt plain (list -> plain needs nothing else; plain -> list needs the annotation steps), then recompute the ref with datagen/scripts/ref.py.

Source: AgentNet (xlangai/AgentNet) trajectories, annotated with claude-sonnet-5-5 (v7 whole x diff); image negatives synthesised by image-neg (OmniParser-v2 + EasyOCR elements, retrieval bank, render_neg). Packed 2026-11-04T18:30:52.