---
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.