Release GUI-JEPA final checkpoints

Final seed-3 GUI-JEPA encoder, policy and text rankers, compatible with GitHub code c03c565. Includes model card, configuration, tokenizers, evaluation summary and SHA-256 checksums.

.gitattributes

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LICENSE

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+MIT License
+
+Copyright (c) 2026 Bingchao Wang and the GUI-JEPA authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,3 +1,131 @@
 ---
+language:
+  - en
 license: mit
+base_model:
+  - timm/vit_large_patch16_224.augreg_in21k_ft_in1k
+  - sentence-transformers/all-MiniLM-L12-v2
+  - sentence-transformers/all-MiniLM-L6-v2
+tags:
+  - gui-agent
+  - mobile-gui
+  - jepa
+  - pytorch
+  - androidcontrol
+  - arxiv:2610.36877
 ---
+
+<div align="center">
+
+# GUI-JEPA
+
+### Candidate-Conditioned JEPA Latents as a Fast Action Policy for Mobile GUI Agents
+
+[![Paper](https://img.shields.io/badge/arXiv-2610.36877-b31b1b)](https://arxiv.org/abs/2610.36877)
+[![Code](https://img.shields.io/badge/GitHub-GUI--JEPA-181717?logo=github)](https://github.com/bcwang-sjtu/GUI-JEPA)
+[![License](https://img.shields.io/badge/License-MIT-16a34a)](LICENSE)
+
+**Bingchao Wang · Yongcheng Jing · Shunyu Liu · Chengyu Wang · Jun Huang · Jie Yang · Wei Liu · Dacheng Tao**
+
+<img src="assets/teaser.png" width="560" alt="GUI-JEPA accuracy and inference speed">
+
+</div>
+
+GUI-JEPA selects mobile GUI actions by scoring candidate-conditioned JEPA latents in a single batched pass. The complete system has approximately **0.50B parameters**, including its frozen CLIP text encoder, and runs **2.8–4.0× faster** than a same-protocol Qwen2.5-VL-7B reference served with vLLM on one NVIDIA H20.
+
+This repository contains the **final seed-3 checkpoint**, serving both AC-Low and AC-High with one policy. The included encoder, policy, span ranker and click reranker reproduce **84.06 / 60.99** IDD step accuracy with the released evaluation code.
+
+## Model components
+
+| Component | Location | Contents |
+|:--|:--|:--|
+| JEPA visual encoder | `encoder/encoder_1.pt` | Frozen ViT-L/16 after GUI screenshot pretraining |
+| Action policy | `policy/heads.pt` | Jointly trained residual predictor, structural head and text-matcher head |
+| Policy text matcher | `policy/matcher_lm/` | Fine-tuned MiniLM-L12 and tokenizer |
+| Goal-source span ranker | `span_ranker/` | Fine-tuned MiniLM-L6, tokenizer and scoring head for `input_text` |
+| Type-gated click reranker | `click_reranker/` | Fine-tuned MiniLM-L12, tokenizer, scoring head and metadata |
+| Application vocabulary | `metadata/app_vocab.json` | Ordered app vocabulary used by the `open_app` argument heuristic |
+
+The model definitions, loaders and evaluation scripts match [code commit `c03c565`](https://github.com/bcwang-sjtu/GUI-JEPA/tree/c03c565065a8b31f9d4ce1be6c29104c8869799f).
+
+The inference settings are recorded in [`gui_jepa_config.json`](gui_jepa_config.json). File sizes and SHA-256 checksums are recorded in [`manifest.json`](manifest.json) and [`SHA256SUMS`](SHA256SUMS).
+
+The frozen [`openai/clip-vit-base-patch32`](https://huggingface.co/openai/clip-vit-base-patch32) text encoder is loaded from its upstream repository. The evaluation and preprocessing scripts also initialize the MiniLM modules from their upstream repositories before replacing the trained weights with these checkpoints. An internet connection or an existing local Hugging Face cache is needed on the first run.
+
+## Method
+
+<p align="center">
+<img src="assets/overview.png" width="100%" alt="GUI-JEPA model architecture">
+</p>
+
+The screenshot is resized directly to 224 × 224, normalized with ImageNet mean and standard deviation, and encoded into a 14 × 14 grid of 1,024-dimensional patch tokens. A residual predictor conditions this grid on every executable candidate action and its region of interest. A structural head and MiniLM text matcher score the candidates. The span ranker fills typed text from the goal; the click reranker refines click selection.
+
+Use the preprocessing in the code repository to preserve the evaluation protocol. The default classification transform associated with the upstream timm backbone uses different normalization and cropping.
+
+## Download and evaluate
+
+Install the [code repository](https://github.com/bcwang-sjtu/GUI-JEPA) and its dependencies. Download the complete model bundle:
+
+```bash
+pip install -U huggingface_hub
+export HF_REPO_ID=LilCC/GUI-JEPA
+hf download "$HF_REPO_ID" --local-dir checkpoints/GUI-JEPA
+export CKPT="$PWD/checkpoints/GUI-JEPA"
+```
+
+Prepare AndroidControl and its feature cache using the steps in the code README. For feature extraction, use the downloaded visual encoder:
+
+```bash
+export AC=/path/to/android_control
+export CACHE=/path/to/ac_cache
+SHARDS="$AC/android_control-*-of-*"
+
+python scripts/preprocess/dump_texts.py --shards "$SHARDS" --splits "$AC/splits.json" --out-dir "$CACHE"
+python scripts/preprocess/dump_candidate_texts.py --shards "$SHARDS" --out "$CACHE/cand_texts.json"
+python scripts/preprocess/build_cache.py --shards "$SHARDS" --splits-dir "$AC" \
+    --encoder-ckpt "$CKPT/encoder/encoder_1.pt" --out "$CACHE"
+
+python scripts/train_policy.py --cache "$CACHE" --eval-only \
+    --ckpt-dir "$CKPT/policy" --span-ranker "$CKPT/span_ranker" \
+    --click-reranker "$CKPT/click_reranker" --app-vocab "$CKPT/metadata/app_vocab.json" \
+    --span-max-words 16 --span-cap 800 --rerank-beta 1 --rerank-gate 1 \
+    --seed 1 --subsplit IDD --out outputs/eval_IDD
+```
+
+These commands run from the root of the code repository. Evaluation requires the AndroidControl cache and matching instruction and candidate-text files. `hf download` preserves the directory structure expected by the scripts. Full-system loading uses the `gui_jepa` modules and these scripts; the top-level bundle is a custom architecture rather than a single Transformers `AutoModel`.
+
+The policy predictor is already stored in `policy/heads.pt`; evaluation does not require the separate dynamics-pretraining initializer. Use `--subsplit full` for the full test set. The model uses one-step prediction with ground-truth action history under the paper's offline evaluation protocol.
+
+## Results
+
+| Evaluation | Episodes | Steps | AC-Low | AC-High |
+|:--|--:|--:|--:|--:|
+| IDD, released seed-3 checkpoint | 716 | 3,858 | **84.06** | **60.99** |
+| Full test, seed 3 (reported in the paper) | 1,543 | 8,444 | **78.64** | **53.75** |
+
+Accuracy is step accuracy in percent under the exact-match judge. AC-Low includes the step instruction; AC-High uses the goal. The IDD values above were reproduced with the released code on 2026-10-29. Detailed metrics are in [`evaluation/idd.json`](evaluation/idd.json). These are single-checkpoint results; the paper also reports three-seed averages.
+
+## Training
+
+The encoder was initialized from an ImageNet-pretrained ViT-L/16 and pretrained on 189K unlabelled GUI-Odyssey screenshots and VideoCUA frames. It remains frozen during policy training. The policy was trained on the AndroidControl training set for six epochs with seed 3, an AdamW learning rate of `1e-4` for scoring heads and `2e-5` for MiniLM and the predictor, weight decay `0.01`, and non-click loss weight `1.5`.
+
+The final predictor uses three Transformer blocks and residual candidate conditioning. The span ranker takes spans from the goal with a maximum of 16 words and a cap of 800 candidates. The click reranker uses 11 auxiliary geometry/flag features, fusion weight `1.0` and confidence gate `1.0`.
+
+## Intended use and scope
+
+The weights support research on candidate-based mobile GUI policies, AndroidControl evaluation and model latency. Inputs include a screenshot, accessibility-tree candidates, a task goal and action history. The benchmark results measure offline step selection; they do not establish closed-loop task completion. English goal text and the AndroidControl action space are the evaluated setting.
+
+## License and upstream models
+
+GUI-JEPA contributions are released under the [MIT License](LICENSE). The visual-backbone initialization and MiniLM derivatives retain their upstream Apache-2.0 notices and conditions; see [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md) and [`licenses/Apache-2.0.txt`](licenses/Apache-2.0.txt). The weights in this repository were adapted through the GUI-JEPA training procedures described above. External models and datasets retain their own licenses.
+
+## Citation
+
+```bibtex
+@article{wang2026guijepa,
+  title   = {{GUI-JEPA}: Candidate-Conditioned {JEPA} Latents as a Fast Action Policy for Mobile {GUI} Agents},
+  author  = {Wang, Bingchao and Jing, Yongcheng and Liu, Shunyu and Wang, Chengyu and Huang, Jun and Yang, Jie and Liu, Wei and Tao, Dacheng},
+  journal = {arXiv preprint arXiv:2610.36877},
+  year    = {2026}
+}
+```

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THIRD_PARTY_NOTICES.md

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+# Third-party model notices
+
+GUI-JEPA contributions are covered by the MIT license in `LICENSE`. The following pretrained models were used to initialize included weights, and their Apache-2.0 license notices and conditions remain applicable. A copy of Apache-2.0 is included in `licenses/Apache-2.0.txt`.
+
+| Included component | Upstream model | Upstream license | GUI-JEPA adaptation |
+|:--|:--|:--|:--|
+| `encoder/encoder_1.pt` | [timm/vit_large_patch16_224.augreg_in21k_ft_in1k](https://huggingface.co/timm/vit_large_patch16_224.augreg_in21k_ft_in1k) | Apache-2.0 | GUI screenshot JEPA pretraining; classification head removed |
+| `policy/matcher_lm/` | [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | Apache-2.0 | Fine-tuned as the policy cross-encoder text matcher |
+| `span_ranker/ranker_lm/` | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | Apache-2.0 | Fine-tuned for goal-source span ranking |
+| `click_reranker/reranker_lm/` | [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | Apache-2.0 | Fine-tuned for click candidate reranking |
+
+The corresponding tokenizer assets are derived from the same upstream repositories. We thank the Google Vision Transformer, timm, Microsoft MiniLM and sentence-transformers authors. The upstream model cards link the original papers, attribution and licensing information.
+
+The frozen CLIP text encoder (`openai/clip-vit-base-patch32`) is an external runtime dependency and is downloaded separately. AndroidControl, GUI-Odyssey and VideoCUA are training/evaluation data sources and are distributed by their respective owners.

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+  "use_roi": false,
+  "roi_dim": 1024
+}

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+This model was initialized from sentence-transformers/all-MiniLM-L12-v2 (Apache-2.0) and modified through GUI-JEPA fine-tuning for click reranking. The tokenizer comes from the same upstream repository. See ../../THIRD_PARTY_NOTICES.md and ../../licenses/Apache-2.0.txt.

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+  "unk_token": "[UNK]"
+}

encoder/NOTICE.txt

@@ -0,0 +1 @@
+This encoder was initialized from timm/vit_large_patch16_224.augreg_in21k_ft_in1k (Apache-2.0) and modified through GUI-JEPA GUI screenshot pretraining. See ../THIRD_PARTY_NOTICES.md and ../licenses/Apache-2.0.txt.

encoder/encoder_1.pt

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+oid sha256:b3c7e5688494c88790a52a506b0b1bcd4085157af6339a1a00d288941abe5b7c
+size 1213321675

evaluation/idd.json

@@ -0,0 +1,114 @@
+{
+  "model": "GUI-JEPA",
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+  "dataset": "AndroidControl",
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+  "steps": 3858,
+  "precision": "fp32",
+  "verified_date": "2026-10-29",
+  "history_source": "ground-truth actions",
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+}

gui_jepa_config.json

@@ -0,0 +1,120 @@
+{
+  "format_version": 1,
+  "model_name": "GUI-JEPA",
+  "paper": "https://arxiv.org/abs/2610.36877",
+  "code": "https://github.com/bcwang-sjtu/GUI-JEPA",
+  "checkpoint_variant": "final policy, seed 3",
+  "training_seed": 3,
+  "evaluation_seed": 1,
+  "encoder": {
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+    "architecture": "vit_large_patch16_224",
+    "initialization": "timm/vit_large_patch16_224.augreg_in21k_ft_in1k",
+    "image_size": [
+      224,
+      224
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+    "grid_size": [
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+  },
+  "policy": {
+    "path": "policy",
+    "heads_file": "heads.pt",
+    "heads_keys": [
+      "wm_head",
+      "matcher_head",
+      "predictor"
+    ],
+    "matcher_path": "policy/matcher_lm",
+    "matcher_base_model": "sentence-transformers/all-MiniLM-L12-v2",
+    "matcher_max_length": 96,
+    "predictor_depth": 3,
+    "predictor_attention_heads": 16,
+    "candidate_action_dim": 16,
+    "predictor_action_input_dim": 1040,
+    "residual": true,
+    "history": true,
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+    "structural_projection_dim": 256,
+    "action_kinds": [
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+      "input_text",
+      "scroll",
+      "navigate_back",
+      "navigate_home",
+      "wait",
+      "open_app"
+    ]
+  },
+  "span_ranker": {
+    "path": "span_ranker",
+    "head_file": "ranker_head.pt",
+    "language_model_path": "span_ranker/ranker_lm",
+    "base_model": "sentence-transformers/all-MiniLM-L6-v2",
+    "max_words": 16,
+    "span_cap": 800,
+    "source": "goal",
+    "max_length": 96
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+  "click_reranker": {
+    "path": "click_reranker",
+    "head_file": "reranker_head.pt",
+    "language_model_path": "click_reranker/reranker_lm",
+    "base_model": "sentence-transformers/all-MiniLM-L12-v2",
+    "aux_dim": 11,
+    "use_roi": false,
+    "beta": 1.0,
+    "gate": 1.0,
+    "max_length": 96
+  },
+  "external_frozen_text_encoder": {
+    "repo_id": "openai/clip-vit-base-patch32",
+    "output_dim": 512,
+    "max_length": 77,
+    "included": false
+  },
+  "app_vocab": "metadata/app_vocab.json",
+  "max_accessibility_candidates": 80,
+  "evaluation": {
+    "precision": "fp32",
+    "settings": [
+      "AC-Low",
+      "AC-High"
+    ],
+    "history_source": "ground-truth actions",
+    "subsplit": "IDD",
+    "episodes": 716,
+    "steps": 3858,
+    "seed": 1
+  },
+  "verified_environment": {
+    "python": "3.11.14",
+    "torch": "2.9.0+cu128",
+    "timm": "1.0.22",
+    "transformers": "4.57.3"
+  },
+  "code_revision": "c03c565065a8b31f9d4ce1be6c29104c8869799f",
+  "model_repo_id": "LilCC/GUI-JEPA"
+}

licenses/Apache-2.0.txt

@@ -0,0 +1,202 @@
+
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+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
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+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
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+
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+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
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+   limitations under the License.

manifest.json

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+{
+  "model": "GUI-JEPA",
+  "variant": "final seed-3 policy",
+  "format_version": 1,
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policy/heads.pt

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policy/matcher_lm/NOTICE.txt

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+This model was initialized from sentence-transformers/all-MiniLM-L12-v2 (Apache-2.0) and modified through GUI-JEPA fine-tuning for policy text matching. The tokenizer comes from the same upstream repository. See ../../THIRD_PARTY_NOTICES.md and ../../licenses/Apache-2.0.txt.

policy/matcher_lm/config.json

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+  "position_embedding_type": "absolute",
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+}

policy/matcher_lm/model.safetensors

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policy/matcher_lm/special_tokens_map.json

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policy/matcher_lm/tokenizer_config.json

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span_ranker/ranker_head.pt

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+size 3461

span_ranker/ranker_lm/NOTICE.txt

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+This model was initialized from sentence-transformers/all-MiniLM-L6-v2 (Apache-2.0) and modified through GUI-JEPA fine-tuning for goal-source span ranking. The tokenizer comes from the same upstream repository. See ../../THIRD_PARTY_NOTICES.md and ../../licenses/Apache-2.0.txt.

span_ranker/ranker_lm/config.json

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+}

span_ranker/ranker_lm/model.safetensors

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span_ranker/ranker_lm/special_tokens_map.json

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+}

span_ranker/ranker_lm/tokenizer_config.json

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