| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - other |
| tags: |
| - agents |
| - world-models |
| - planning |
| - tool-use |
| - synthetic |
| - reproducible-research |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: mini_tasks.jsonl |
| --- |
| |
| # MiniOS — a deterministic symbolic task suite for tool-agent world models |
|
|
| This dataset is the **MiniOS** task suite from **nanoAWM**. Code, the world |
| model trained on it, and the full paper live in the GitHub repository: |
| **https://github.com/jlov7/nanoAWM**. |
|
|
| MiniOS is a deterministic, fully symbolic toy operating system: filesystem, |
| terminal/tests, git, browser/forms, DB/table, email/calendar, package/config, |
| approvals, hidden state, irreversible actions, delayed consequences, and |
| rollback. Each task is constructed to exhibit **consequence aliasing** — two |
| candidate actions that look equally reasonable from the current visible |
| observation but diverge sharply in future reward, reversibility, hidden-state |
| corruption, or approval compliance. A memoryless reactive policy provably cannot |
| be optimal across the hidden states; a history-conditioned consequence model |
| can. |
|
|
| ## Contents |
|
|
| `mini_tasks.jsonl` — **420 tasks across 15 families**, deterministically |
| generated (seeded). |
|
|
| | Split | Tasks | Purpose | |
| | --- | ---: | --- | |
| | `train` | 240 | training (used with 3 vocabulary-randomized variants each → 720) | |
| | `val` | 30 | validation / calibration only | |
| | `test` | 60 | in-distribution evaluation | |
| | `template_holdout` | 45 | in-distribution evaluation (see disclosure below) | |
| | `ood` | 45 | local out-of-distribution stress | |
|
|
| The 15 families include approval-gated email send, irreversible file deletion, |
| delayed test failure, database-migration hidden invariant, git dirty-tree, |
| rollback recovery, package-install dependency conflict, cross-surface |
| dependency, ambiguous same-visible-observation, and adversarial failure |
| variants. |
|
|
| ### Schema (one JSON object per line) |
|
|
| | Field | Meaning | |
| | --- | --- | |
| | `task_id`, `template_id`, `seed` | identity / generation provenance | |
| | `family`, `split` | task family and split membership | |
| | `description` | the natural-language task given to the agent | |
| | `visible_state` | the observation the agent sees (surface, object, phase, …) | |
| | `actions` | the candidate action set | |
| | `hidden_state` | environment ground truth (approval status, corruption, …) | |
| | `reactive_action` | the action a memoryless reactive policy would pick | |
|
|
| **`hidden_state` is environment ground truth, not a planning-time input.** It is |
| needed to *run* the environment and score outcomes, but the learned planner's |
| feature extractor explicitly excludes it (along with task/split ids, labels, |
| rewards, and oracle next-state). Shipping it here lets you reproduce the |
| environment; it is not a leak into the agent. |
| |
| ## Honest disclosure: what these splits are, and are not |
| |
| This dataset is **partly lexically separable** and the eval splits **share |
| vocabulary with training**. Treat the split names literally and skeptically: |
| |
| - Measured feature-level overlap with a training task: **`test` 90%, |
| `template_holdout` 100%, `ood` 71%**. The split named `template_holdout` does |
| **not** hold out templates. |
| - Treat "held-out 1.000" in the paper as *in-distribution* performance. The |
| honest generalization measurement rebuilds the suite over a **disjoint |
| vocabulary** (held-out object names *and* disjoint nonce action markers |
| sharing no tokens with training) — on which the learned planner scores |
| **0.524** vs a 0.067 best baseline and a 1.000 oracle. |
| - The safe/risky distinction is partly encoded in action strings, so a pure |
| keyword matcher with no world model already scores ~0.73 here. A large part of |
| the task is lexical, not consequence reasoning. |
|
|
| This is local symbolic evidence for studying consequence modeling under hidden |
| state — **not** a web/OS benchmark and **not** external validation. |
|
|
| ## Usage |
|
|
| ```python |
| import json |
| tasks = [json.loads(line) for line in open("mini_tasks.jsonl")] |
| train = [t for t in tasks if t["split"] == "train"] |
| ``` |
|
|
| Regenerate deterministically (CPU-only, no network) from the GitHub repo: |
|
|
| ```bash |
| python -m nanoawm.envs.generate --suite mini --out data/mini_tasks.jsonl |
| ``` |
|
|
| The disjoint-vocabulary generalization suite (the source of the honest 0.524 |
| number) is produced by `python -m nanoawm generalization_ladder --run-ladder`. |
|
|
| ## Citation |
|
|
| ``` |
| @software{nanoawm, |
| title = {nanoAWM: a tiny action-conditioned world model for tool agents}, |
| author = {Lovell, Jason}, |
| year = {2026}, |
| url = {https://github.com/jlov7/nanoAWM} |
| } |
| ``` |
|
|
| License: MIT. |
|
|
| ## Personal project disclaimer |
|
|
| <sub>Personal, independent research and development. Not affiliated with, endorsed by, or representative of any employer, client, or organization.</sub> |
|
|
