Add image-text-to-text task category and improve metadata
#2
by nielsr HF Staff - opened
README.md
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---
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license: mit
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language:
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- en
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size_categories:
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- n<1K
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tags:
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- arxiv:2606.16748
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- computer-use
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- agents
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- benchmark
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**A benchmark for personally intelligent computer-use agents.**
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This dataset
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[MyPCBench](https://github.com/ljang0/MyPCBench). It accompanies the VM image
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[`ljang0/mypcbench-qemu-baseline`](https://huggingface.co/ljang0/mypcbench-qemu-baseline)
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and the [paper](https://huggingface.co/papers/2606.16748).
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**cross-linked**, so one trip leaves correlated records across every app that
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would plausibly book it. Agents are evaluated on tasks that require **knowing
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who the user is**.
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## Loading
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## Contents
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- **184 tasks**, all rubric-graded (LLM-as-judge).
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- `data/tasks.jsonl` — the dataset split the viewer and `load_dataset` use. One
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row per task, with grading rubrics inlined and the paper's behavioural-type
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label merged in.
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- `raw/` — the verbatim source files from the GitHub repo, for fidelity:
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- `all_tasks_with_grading.json` — flat eval input (grading inlined).
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- `all_tasks.json` — convenience flat list **without** grading (do not grade with this).
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## Grading
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Grading is **rubric-only** (LLM-as-judge). The runner records episode
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completion; an offline judge scores each rubric over the full trajectory. See
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the [GitHub repo](https://github.com/ljang0/MyPCBench) for the runner and judge.
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## Citation
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```bibtex
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@misc{jang2026mypcbench,
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title = {MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents},
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author = {Jang, Lawrence},
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year = {2026},
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url = {https://huggingface.co/papers/2606.16748}
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}
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```
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License: MIT.
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---
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language:
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- en
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license: mit
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size_categories:
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- n<1K
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pretty_name: MyPCBench Tasks
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task_categories:
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- image-text-to-text
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tags:
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- computer-use
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- agents
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- benchmark
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**A benchmark for personally intelligent computer-use agents.**
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This dataset provides the task set and grading rubrics for [MyPCBench](https://github.com/ljang0/MyPCBench).
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- **Paper:** [MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents](https://huggingface.co/papers/2606.16748)
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- **Project Page:** [https://mypcbench.com/](https://mypcbench.com/)
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- **GitHub:** [https://github.com/ljang0/MyPCBench](https://github.com/ljang0/MyPCBench)
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- **VM Image:** [`ljang0/mypcbench-qemu-baseline`](https://huggingface.co/ljang0/mypcbench-qemu-baseline)
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MyPCBench is a reproducible Linux-desktop benchmark seeded end-to-end from one canonical persona (Michael Scott, *The Office*). The image hosts 17 pre-logged-in web apps mirroring real consumer products plus the full LibreOffice suite. The persona's records (bank transactions, emails, calendar events, chat messages, retail/grocery/food orders, web visits, rides) are **cross-linked**, so one trip leaves correlated records across every app that would plausibly book it. Agents are evaluated on tasks that require **knowing who the user is**.
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## Loading
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## Contents
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- **184 tasks**, all rubric-graded (LLM-as-judge).
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- `data/tasks.jsonl` — the dataset split the viewer and `load_dataset` use. One row per task, with grading rubrics inlined and the paper's behavioural-type label merged in.
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- `raw/` — the verbatim source files from the GitHub repo, for fidelity:
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- `all_tasks_with_grading.json` — flat eval input (grading inlined).
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- `all_tasks.json` — convenience flat list **without** grading (do not grade with this).
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## Grading
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Grading is **rubric-only** (LLM-as-judge). The runner records episode completion; an offline judge scores each rubric over the full trajectory. See the [GitHub repo](https://github.com/ljang0/MyPCBench) for the runner and judge.
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## Citation
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```bibtex
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@misc{jang2026mypcbench,
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title = {MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents},
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author = {Jang, Lawrence Keunho and Jang, Andrew Keunwoo and Koh, Jing Yu and Salakhutdinov, Ruslan},
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year = {2026},
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url = {https://huggingface.co/papers/2606.16748}
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}
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```
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