Add MiniOS task suite + dataset card

README.md

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+---
+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.