| --- |
| license: other |
| license_name: mixed-see-card |
| language: |
| - en |
| pretty_name: OpenJev-Data |
| tags: |
| - jev |
| - jev-alternative |
| - open-source-jev |
| - browser-agent |
| - web-navigation |
| - decision-model |
| - system-one |
| - distillation |
| - benchmark |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: mind2web |
| data_files: |
| - {split: train, path: mind2web/train.parquet} |
| - {split: validation, path: mind2web/validation.parquet} |
| - {split: test, path: mind2web/test.parquet} |
| - config_name: nnetnav_audited |
| data_files: |
| - {split: train, path: nnetnav_audited/train.parquet} |
| - {split: validation, path: nnetnav_audited/validation.parquet} |
| - {split: test, path: nnetnav_audited/test.parquet} |
| - config_name: webchain |
| data_files: |
| - {split: train, path: webchain/train.parquet} |
| - {split: validation, path: webchain/validation.parquet} |
| - {split: test, path: webchain/test.parquet} |
| - config_name: teacher_qwen3max |
| data_files: |
| - {split: train, path: teacher_qwen3max/train.parquet} |
| - {split: validation, path: teacher_qwen3max/validation.parquet} |
| - {split: test, path: teacher_qwen3max/test.parquet} |
| - config_name: teacher_glm |
| data_files: |
| - {split: train, path: teacher_glm/train.parquet} |
| - {split: validation, path: teacher_glm/validation.parquet} |
| - {split: test, path: teacher_glm/test.parquet} |
| - config_name: onpolicy_glm |
| data_files: |
| - {split: train, path: onpolicy_glm/train.parquet} |
| - {split: validation, path: onpolicy_glm/validation.parquet} |
| - {split: test, path: onpolicy_glm/test.parquet} |
| - config_name: live_tasks |
| data_files: |
| - {split: train, path: live_tasks/train.parquet} |
| - {split: test, path: live_tasks/test.parquet} |
| - config_name: bench |
| data_files: |
| - {split: test, path: bench/test.parquet} |
| --- |
| |
| # OpenJev-Data |
|
|
| **Jun Huang\*, Xin Ren\*** · University of Electronic Science and Technology of China · \*Equal contribution |
| |
| **OpenJev** is an open-source, local alternative to Jev's System One decision API: same request shape, your own GPU, no API key. |
| |
| Browser-step data and a benchmark for **decision models**: the small, fast models that pick a web agent's next |
| operation and target element. Every row is one browser step, written as a `POST /v1/systemone` request (the page |
| state plus typed questions) with its labelled answers. The requests use exactly the format the open browser agent |
| [jev-ultrafast](https://github.com/browser-use/jev-ultrafast) sends to its System One, so a model trained or scored |
| here can be served behind that agent unchanged. The data trains the [OpenJev](https://github.com/alanhuangyoo/OpenJev) models |
| ([OpenJev-1.7B](https://huggingface.co/alanhuangya/OpenJev-1.7B), [OpenJev-4B](https://huggingface.co/alanhuangya/OpenJev-4B), |
| [OpenJev-8B](https://huggingface.co/alanhuangya/OpenJev-8B)), and the `bench` config is a frozen test set, **OpenJev-Bench**, that |
| scores any System One endpoint. |
| |
| ## Load it |
| |
| Every config is parquet and loads with one call: |
| |
| ```python |
| from datasets import load_dataset |
| |
| m2w = load_dataset("alanhuangya/OpenJev-Data", "mind2web") # human steps, split by website |
| nnetnav = load_dataset("alanhuangya/OpenJev-Data", "nnetnav_audited") # live-web steps with audited DONE labels |
| webchain = load_dataset("alanhuangya/OpenJev-Data", "webchain") # human steps on 200+ live sites |
| qwen = load_dataset("alanhuangya/OpenJev-Data", "teacher_qwen3max") # qwen3-max acting as System One |
| glm = load_dataset("alanhuangya/OpenJev-Data", "teacher_glm") # GLM-5.3-Flash acting as System One |
| onpolicy = load_dataset("alanhuangya/OpenJev-Data", "onpolicy_glm") # student-visited states, GLM labels |
| tasks = load_dataset("alanhuangya/OpenJev-Data", "live_tasks") # goals + start URLs; test = 153 held-out tasks |
| bench = load_dataset("alanhuangya/OpenJev-Data", "bench", split="test") # OpenJev-Bench, frozen |
| ``` |
| |
| ## Configs |
| |
| | Config | Rows (train / validation / test) | Source | License | |
| |---|---|---|---| |
| | `mind2web` | 5,861 / 586 / 875 | [Mind2Web](https://huggingface.co/datasets/osunlp/Mind2Web), converted | CC BY 4.0 | |
| | `nnetnav_audited` | 11,408 / 1,140 / 1,150 | [NNetNav-live](https://huggingface.co/datasets/stanfordnlp/nnetnav-live), converted, stopping labels audited | Apache-2.0 | |
| | `webchain` | 17,876 / 1,069 / 1,589 | [WebChain](https://huggingface.co/datasets/webagentlab/webchain) v2, converted from its HTML snapshots | CC BY 4.0 | |
| | `teacher_qwen3max` | 4,781 / 385 / 57 | qwen3-max as the System One of jev-ultrafast on live sites (two collections) | see *Terms* | |
| | `teacher_glm` | 5,969 / 370 / 147 | GLM-5.3-Flash in the same role | see *Terms* | |
| | `onpolicy_glm` | 5,782 / 275 / 119 | DAgger-style: an OpenJev student or the GLM teacher acts, the GLM teacher labels | see *Terms* | |
| | `live_tasks` | 1,402 / – / 153 | goals and start URLs for live runs; `test` is the held-out end-to-end suite | see *Terms* | |
| | `bench` | – / – / 2,346 | OpenJev-Bench: frozen test steps from the configs above | per row's source | |
|
|
| In v1 the two qwen3-max collections were separate configs (`teacher_first`, `teacher_second`); they are now one |
| config with a `collection` column (`teacher-v1`, `teacher-v2`). v1 shipped JSONL; v2 ships parquet. |
|
|
| The general typed-decision corpora used in training (Kev decision-v7, typed-decisions, tasksource-jev, |
| jev-distill-corpus-v3, typed-decisions-synth) are not redistributed here; the repository's builders download and |
| convert them from their sources. |
|
|
| ## Columns |
|
|
| Step configs (all but `live_tasks`) share one schema: |
|
|
| | Column | Meaning | |
| |---|---| |
| | `request` | the full `POST /v1/systemone` request as a JSON string; send it as is to replay the step | |
| | `labels` | JSON string: the gold option key per labelled question, e.g. `{"operation": "CLICK", "click_target": "7"}` | |
| | `meta` | JSON string: the source's own metadata (for teacher rows: episode, task, teacher, its one-sentence reason) | |
| | `id` | stable row id (hash of request and labels) | |
| | `url`, `title`, `goal` | page URL and title, task goal | |
| | `n_elements`, `n_recent_actions` | candidate elements offered (8–40 for converted data), actions in the history | |
| | `operation` | gold operation: CLICK, TYPE_TEXT, SELECT, SCROLL_DOWN, SCROLL_UP, WAIT, DONE or BLOCKED | |
| | `target_question`, `target`, `target_element` | the labelled target question (`click_target`, ...), its gold key and the element's text | |
| | `source`, `collection` | origin dataset and the collection it was built in (`m2w-v2`, `teacher-v3-glm`, `dagger-g1`, ...) | |
| | `website`, `task_id`, `episode_id` | site, task and episode (or trajectory) ids | |
| | `actor` | `onpolicy_glm` only: who acted at this step, `student` or `teacher` | |
| | `teacher`, `reason` | teacher rows: teacher model and its stated reason | |
|
|
| `bench` adds `bench_subset`, the config each row comes from. `live_tasks` has `id`, `url`, `goal`, `source`. |
|
|
| A request looks like this (shortened): |
|
|
| ```json |
| { |
| "model": "wev-latest", |
| "state": { |
| "page": {"url": "...", "title": "...", "text": "visible page text"}, |
| "elements": [{"index": "1", "role": "button", "label": "Search", "operations": ["CLICK"]}], |
| "recent_actions": [{"action": "...", "kind": "click", "text": null, "page_changed": true}] |
| }, |
| "questions": { |
| "operation": {"type": "choice", |
| "instructions": {"goal": "...", "rules": ["..."]}, |
| "criteria": {"CLICK": "...", "TYPE_TEXT": "...", "DONE": "...", "BLOCKED": "..."}}, |
| "click_target": {"type": "choice", |
| "instructions": {"goal": "...", "operation": "CLICK", "rules": ["..."]}, |
| "criteria": {"1": {"element": "[1] Search", "current_value": "", "role": "button"}}} |
| } |
| } |
| ``` |
|
|
| A step asks for the operation and, for each operation that needs one, a target (`click_target`, `type_text_target` |
| or `select_target`). A step is answered correctly when the operation and, if the operation takes one, its target both |
| match. Targets are chosen among the candidates listed in the state, not among every element on the page. To train |
| with the OpenJev code (the `wev` package), pass JSONL folders to `wev train --data`; `scripts/build_hf_dataset.py` documents the mapping. |
|
|
| ## How the configs were built |
|
|
| **mind2web.** Each recorded step becomes one request containing the page's visible text, a sample of 8–40 |
| candidate elements that includes the gold element, the task and the preceding actions. Splits are by website, so every |
| test website is unseen in training. Mind2Web has no DONE, WAIT, SCROLL or BLOCKED steps. |
|
|
| **nnetnav_audited.** NNetNav supplies the stopping (DONE), giving-up (BLOCKED) and scrolling steps that Mind2Web |
| lacks. Its trajectories come from unsupervised exploration, so its stopping labels are noisy. An LLM judge |
| (DeepSeek-V4.1-Flash) read the goal and the state at every step and decided whether the goal was already achieved. |
| On the training split it rejected 592 of 1,898 DONE labels (31%) and found the goal already achieved at 2,281 of the |
| 10,102 other steps (23%). Those steps were relabelled DONE and the rejected DONE steps were dropped. The validation |
| and test splits were audited the same way. |
| |
| **webchain.** WebChain v2 releases screenshots and multimodal SFT conversations, but its action metadata also links, |
| for most steps, a pre-action HTML snapshot and an accessibility tree together with the CSS selector of the element |
| acted on. `wev/webchain.py` resolves that selector in the snapshot (unique matches only, climbing from e.g. a `<span>` |
| to its `<button>`), samples 8–40 negatives from the snapshot's other visible interactive elements, and renders the |
| request exactly as for Mind2Web. Clicks, hovers and double clicks become CLICK, typing and pasting TYPE_TEXT. 4,000 |
| of WebChain's 31,445 English-goal trajectories were converted (a fixed hash order); pages not in English, steps |
| without a snapshot, unresolvable selectors and custom (non-`<select>`) dropdowns were skipped. Splits are by host |
| (175 / 15 / 16 hosts). Like Mind2Web, it has no DONE, WAIT, SCROLL or BLOCKED steps. |
| |
| **teacher_qwen3max, teacher_glm.** A prompted LLM served as the System One of jev-ultrafast on live websites: |
| qwen3-max for the two collections in `teacher_qwen3max` (the second ran on the tasks the first had not solved), and |
| GLM-5.3-Flash, served through Alibaba Bailian, for `teacher_glm`. The prompt forbade signing in, registering, buying, |
| booking, posting, messaging and submitting personal information, and asked for BLOCKED on CAPTCHAs, unusual-traffic |
| pages and login walls. Every request and the teacher's choice were logged. An LLM judge then read the final page of |
| each episode, and the kept rows are: |
|
|
| - every decision of an episode whose DONE the judge confirmed; |
| - every decision up to a BLOCKED on a page that blocked the agent; |
| - every decision but the last of an episode cut short by the browser harness. |
|
|
| Episodes that looped or exhausted their budget were dropped, as were repeated states. The judge rejected 155 of |
| qwen3-max's 431 DONE claims (36%) and 127 of GLM-5.3-Flash's 530 (24%). |
|
|
| **onpolicy_glm.** Trained only on teacher episodes, a student never sees the states its own mistakes lead to. This |
| config was collected DAgger-style with a mixed policy: at every step both an OpenJev student and the GLM-5.3-Flash teacher |
| answered the request, one of them chosen at random with equal probability acted (`actor`: 2,894 student and 2,896 |
| teacher steps in train), and the teacher's choice was always recorded as the label. An episode's ending was judged |
| from the actions actually executed: the judge-confirmed final state is labelled DONE, earlier teacher DONE labels |
| (unverified) are dropped, and a BLOCKED ending is kept only when the teacher agrees. The judge rejected 135 of 587 |
| DONE endings (23%). Otherwise the filters are those of the teacher configs. |
| |
| **Splits of the teacher configs.** Episodes are split by a hash of the task id, so a task is in the same split in |
| every collection. A held-out task becomes `test` only when neither its task id nor its goal occurs in any train split |
| of any config, and `validation` otherwise. NNetNav-sourced tasks take their goals from NNetNav's train split, so the |
| teacher test splits hold only the Google Flights and Wikipedia tasks: 323 steps from 8 tasks. |
| |
| **live_tasks.** Goals paired with start URLs: NNetNav goals on their live sites, Wikipedia look-ups and Google |
| Flights searches. The 153 `test` tasks are the end-to-end suite used to evaluate the OpenJev models; none of them appears |
| in a teacher config. Checked by task id and by goal against every train split: one eval goal ("Find hotels in San |
| Francisco." on booking.com) also occurs at one step of `nnetnav_audited` train, because NNetNav repeats some goals |
| across its own splits. The 123 NNetNav eval tasks come from NNetNav's test split, so their goals appear in |
| `nnetnav_audited` test (555 steps); that is by construction, not a leak. |
|
|
| ## OpenJev-Bench |
|
|
| `bench` is a frozen test set for browser decision models: 2,346 steps, each a request a System One must answer. |
|
|
| | Subset | Steps | What it tests | |
| |---|---|---| |
| | `mind2web` test | 873 | clicks, typing and dropdowns on 8 websites unseen in training | |
| | `nnetnav_audited` test | 1,150 | stopping (DONE 382), giving up (BLOCKED 29) and scrolling on live sites | |
| | teacher test slices | 323 | qwen3-max (57), GLM (147) and on-policy (119) steps on tasks in no train split | |
|
|
| Two Mind2Web test requests are left out because one question exceeds the API's 255-option limit. |
|
|
| **Metrics.** A step succeeds when the operation and, if the step has one, the target are right. OpenJev-Bench reports |
| step success, operation accuracy, target accuracy, the premature-DONE rate (steps whose gold operation is not DONE |
| that the model answered DONE: an agent that stops early abandons its task), DONE recall, top-label ECE (10 bins; on |
| the probability the model gives its own choice) and p50/p95 latency per request, overall and per subset. Conventions |
| follow [JevBench](https://github.com/fstandhartinger/jevbench): failed or malformed answers count as wrong, and an |
| answer without a distribution is scored as one-hot and flagged. |
|
|
| **Run it** against any endpoint that speaks `POST /v1/systemone`: |
|
|
| ```bash |
| wev serve --model alanhuangya/OpenJev-4B --port 8009 # or any System One server |
| python scripts/wev_bench.py --url http://127.0.0.1:8009/v1/systemone --name my-model \ |
| --hardware "RTX 5090, bf16" --out results/wev-bench/my-model.json |
| ``` |
|
|
| The script is in the [OpenJev repository](https://github.com/alanhuangyoo/OpenJev/blob/main/scripts/wev_bench.py) and needs |
| only the Python standard library and `pyarrow`. The result is a JSON file in JevBench's per-system layout (`key`, |
| `display`, `open`, `licence`, `has_distribution`, a `browser` lane block with `by_family`, `speed_block`, ...), plus |
| the dataset revision and the bench file's sha256, so it can be submitted to a leaderboard as is. |
|
|
| | System One | Step success | Operation | Target | Premature DONE | ECE | p50 / p95 latency | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:| |
| | **OpenJev-4B v0.2** | **73.1** | **84.7** | **81.4** | **3.3** | **0.013** | **153 / 353 ms** | |
| | OpenJev-4B v0.1 | 68.0 | 81.3 | 79.0 | 5.0 | 0.051 | 166 / 409 ms | |
|
|
| Both were served with `wev serve` on one RTX 5090 (bf16) and queried serially from the same host. Their training |
| included the `mind2web`, `nnetnav_audited` and `teacher_qwen3max` train splits (v0.2 also `teacher_glm`), so they are |
| in-distribution there. |
|
|
| ## Personal and sensitive information |
|
|
| All step rows were scanned for e-mail addresses, phone numbers, card-like and SSN-like numbers, API-key, JWT and |
| private-key shapes, credential-like URL parameters, values in password fields, and personal names typed into fields. |
| Most matches are public business contacts in page text and element labels (phone numbers on 2,510 rows, e-mail |
| addresses on 1,525). |
| Text an agent typed: made-up contact details dictated by Mind2Web and NNetNav goals (e.g. an e-mail on gmail.com, |
| example.com or sample.com), names typed into name fields (23 steps, plus 75 where the goal supplies the name), and |
| values typed into password fields by NNetNav's exploring agent (17 steps) and a 4-digit gift-card PIN in Mind2Web |
| (1). URLs: short-lived signed tokens (JWT-shaped `edgeauth`/OAuth `state` values, session ids; 163 rows). Page |
| text: one Hugging Face token in a code sample on a public model page (2 rows) and one store loyalty number (2 rows). |
| No credentials were entered by the teacher, which was instructed never to sign in. |
|
|
| Applied in this release (`scripts/redact.py`, run by `scripts/build_hf_dataset.py`): typed e-mail addresses, phone |
| numbers and personal names not dictated by the goal became `user@example.com`, `555-0100` and `Alex Doe`; values in |
| password fields, API keys and tokens, and credential-like URL parameters became `<redacted>`; other e-mail addresses |
| in page text and labels became `<email>`; the 3 rows with card- or SSN-like numbers were dropped. Published business |
| phone numbers on public pages were kept. Separately, 86 teacher and on-policy steps labelled DONE on a page that had |
| not loaded (no elements, almost no text) were dropped. |
|
|
| ## Terms |
|
|
| | Source | License | |
| |---|---| |
| | Mind2Web-derived rows | CC BY 4.0 | |
| | NNetNav-derived rows | Apache-2.0 | |
| | WebChain-derived rows | CC BY 4.0 | |
| | teacher, on-policy rows and live tasks | see below | |
|
|
| Attribute the original datasets (see *Citation*). The teacher and on-policy configs and the live tasks contain text |
| captured from public websites, which remains subject to those sites' terms. The labels in `teacher_qwen3max` are |
| outputs of qwen3-max, and those in `teacher_glm` and `onpolicy_glm` are outputs of GLM-5.3-Flash (served through |
| Alibaba Bailian); they are subject to their providers' terms. The DONE verdicts and NNetNav relabelling come from an |
| LLM judge (DeepSeek-V4.1-Flash). Treat these configs as research data and check the applicable terms before any |
| commercial use. WebChain's page snapshots were captured by its annotators on live websites and remain subject to |
| those websites' terms; its card asks users not to attempt to recover personal information. |
|
|
| ## Citation |
|
|
| Jun Huang and Xin Ren contributed equally (University of Electronic Science and Technology of China). The paper |
| describes OpenJev under its earlier name, wev. |
|
|
| ```bibtex |
| @misc{huang2026wev, |
| title = {wev: Distilling LLM Browser Agents into Open, Local System-One Decision Models}, |
| author = {Huang, Jun and Ren, Xin}, |
| year = {2026}, |
| publisher = {Zenodo}, |
| doi = {10.5281/zenodo.22941164}, |
| url = {https://doi.org/10.5281/zenodo.22941164} |
| } |
| ``` |
|
|
| If you use the converted configs, please also cite their sources: |
|
|
| ```bibtex |
| @inproceedings{deng2023mind2web, |
| title = {Mind2Web: Towards a Generalist Agent for the Web}, |
| author = {Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su}, |
| booktitle = {Advances in Neural Information Processing Systems}, |
| year = {2023} |
| } |
| @misc{murty2024nnetnav, |
| title = {NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild}, |
| author = {Shikhar Murty and Hao Zhu and Dzmitry Bahdanau and Christopher D. Manning}, |
| year = {2024}, |
| eprint = {2410.02907}, |
| archivePrefix = {arXiv} |
| } |
| @misc{webchain2026, |
| title = {WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces}, |
| author = {Sicheng Fan and Rui Wan and Yifei Leng and Gaoning Liang and Li Ling and Yanyi Shang and Dehan Kong}, |
| year = {2026}, |
| eprint = {2603.05295}, |
| archivePrefix = {arXiv} |
| } |
| ``` |
|
|
