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2026 · Preprint

Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3

Jens Lehmann; Andrei Aioanei; Sahar Vahdati

Tycho constructs editable Python models of game state, transitions, rendering and outcomes from action/observation histories in unfamiliar ARC-AGI-3 games. It checks model predictions against recorded transitions and can use, repair or bypass these models during planning.

Scope and limitations

Evaluation uses one stochastic run per configuration on 25 public games and 183 levels; five games informed harness development. Models may abstain on cells or accept bounded rendering variants. Transition replay and game scores do not establish complete engine reconstruction, correct objectives, unseen-state fidelity, unseen-game generalization or run-to-run reliability; the agent can bypass modeling.

Why it fits this collection

Studies executable reconstruction of observed game dynamics and checks model fidelity separately from task success, providing a bounded behavioral reconstruction case.

Publication and review details

Venue
arXiv
Status
Preprint
Publication / posting date
July 30, 2026
Date source
First public arXiv submission date; version v1 was reviewed.
First arXiv submission
July 30, 2026
arXiv
2607.28287
Reviewed arXiv version
v1
DOI
10.48550/arXiv.2607.28287

The arXiv posting date is July 30; the manuscript header and software release use July 29. DataCite confirms the arXiv DOI is registered and findable. The pinned author artifact is Apache-2.0 and includes source, prompts, paper configurations and compact evaluation evidence; ARC environment terms are separate. Python >=3.12, model/ARC access and a Docker or Finch sandbox are documented requirements; no installation or experiments were run.

Sources reviewed

Source review only; experiments were not rerun for this collection. Cite the original authors using the linked paper or BibTeX.

Primary sources

  1. https://arxiv.org/abs/2607.28287
  2. https://arxiv.org/html/2607.28287v1
  3. https://arxiv.org/pdf/2607.28287v1
  4. https://api.datacite.org/dois/10.48550/arXiv.2607.28287
  5. https://github.com/NIMI-research/Tycho
  6. https://raw.githubusercontent.com/NIMI-research/Tycho/f68912a764372ead0a610db2e1c011d41ce5197e/README.md
  7. https://raw.githubusercontent.com/NIMI-research/Tycho/f68912a764372ead0a610db2e1c011d41ce5197e/LICENSE
  8. https://raw.githubusercontent.com/NIMI-research/Tycho/f68912a764372ead0a610db2e1c011d41ce5197e/pyproject.toml
  9. https://raw.githubusercontent.com/NIMI-research/Tycho/f68912a764372ead0a610db2e1c011d41ce5197e/CITATION.cff
  10. https://raw.githubusercontent.com/NIMI-research/Tycho/f68912a764372ead0a610db2e1c011d41ce5197e/PUBLIC_RELEASE_MANIFEST.json