| --- |
| license: mit |
| language: |
| - en |
| pretty_name: MyPCBench Tasks |
| size_categories: |
| - n<1K |
| tags: |
| - arxiv:2606.16748 |
| - computer-use |
| - agents |
| - benchmark |
| - llm-as-judge |
| - personal-assistant |
| - desktop |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/tasks.jsonl |
| --- |
| |
| # MyPCBench — Task Set |
|
|
| **A benchmark for personally intelligent computer-use agents.** |
|
|
| This dataset is the task set and grading rubrics for |
| [MyPCBench](https://github.com/ljang0/MyPCBench). It accompanies the VM image |
| [`ljang0/mypcbench-qemu-baseline`](https://huggingface.co/ljang0/mypcbench-qemu-baseline) |
| and the [paper](https://huggingface.co/papers/2606.16748). |
|
|
| 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**. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("ljang0/MyPCBench-tasks") # DatasetDict({test: 184 rows}) |
| task = ds["test"][0] |
| print(task["instruction"]) |
| print(task["grading"]["rubrics"]) |
| ``` |
|
|
| ## Contents |
|
|
| - **184 tasks**, all rubric-graded (LLM-as-judge). |
| - `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. |
| - `raw/` — the verbatim source files from the GitHub repo, for fidelity: |
| - `all_tasks_with_grading.json` — flat eval input (grading inlined). |
| - `all_tasks.json` — convenience flat list **without** grading (do not grade with this). |
| - `task_types.json` — per-task behavioural-type mapping (paper taxonomy). |
| - `variables.json` — ground-truth values reference (in-VM paths, balances, …). |
| - `schema.json` — task JSON schema. |
|
|
| ## Fields |
|
|
| Each row has: |
|
|
| | field | description | |
| |---|---| |
| | `id` | unique task id, e.g. `retrieval-f016` | |
| | `app` | primary app the task targets | |
| | `apps_involved` | apps the task is expected to touch (analysis only, not enforced) | |
| | `category` | internal task category (`retrieval`, `aggregation`, `contradiction`, `situated_action`, `preference_inference`, `counterfactual`, `long_horizon`, `cua_only`, `hard_app`) | |
| | `behavioral_type` | paper's six-type taxonomy: `bounded_action` (64), `multi_step_orchestration` (48), `cross_source_reconciliation` (25), `aggregation_reporting` (23), `personal_lookup` (13), `pattern_inference` (11) | |
| | `difficulty` | `easy` / `medium` / `hard` | |
| | `instruction` | the natural-language task instruction given to the agent | |
| | `num_rubrics` | number of grading rubrics (3–13 per task) | |
| | `grading` | `{ "type": "llm_judge", "rubrics": [ { "criterion", "type", "weight" } ] }` — rubric weights sum to 1.0 | |
|
|
| ## Grading |
|
|
| 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. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{jang2026mypcbench, |
| title = {MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents}, |
| author = {Jang, Lawrence}, |
| year = {2026}, |
| url = {https://huggingface.co/papers/2606.16748} |
| } |
| ``` |
|
|
| License: MIT. |
|
|
