jlov7/nanoAWM-minios

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

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:

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

_{Personal, independent research and development. Not affiliated with, endorsed by, or representative of any employer, client, or organization.}