pith:DMNDIYZ6
From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning
Multimodal models solve maze images by converting them to text grids and enumerating paths token by token rather than through visual planning.
arxiv:2603.26839 v2 · 2026-03-27 · cs.LG · cs.CV
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\pithnumber{DMNDIYZ6LD3KV44VFERAOOUZQY}
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Record completeness
Claims
MazeBench therefore shows that high accuracy on visual planning tasks does not imply human-like spatial understanding.
That the two-stage image-to-grid plus token enumeration strategy observed in traces is the dominant mechanism driving performance rather than an artifact of the specific prompting or model configurations tested.
Multimodal models achieve high maze accuracy by translating images to text grids and performing token-level BFS search, not through visual planning.
Formal links
Receipt and verification
| First computed | 2026-05-18T03:09:22.499408Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1b1a34633e58f6aaf3952922073a99861b264bfad4e1494fc56067e07e3f0740
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DMNDIYZ6LD3KV44VFERAOOUZQY \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1b1a34633e58f6aaf3952922073a99861b264bfad4e1494fc56067e07e3f0740
Canonical record JSON
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