pith:KK5YAO5G
Latent Geometry Beyond Search: Amortizing Planning in World Models
In a pretrained world model whose latent space is regularized for smoothness and uniformity, a goal-conditioned inverse dynamics model can replace online search while matching its performance at far lower cost.
arxiv:2605.08732 v2 · 2026-05-09 · cs.RO · cs.LG
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Claims
Under such geometry, planning can be amortized into a latent inverse-dynamics mapping instead of requiring online search. Empirically, the GC-IDM matches or exceeds CEM in seven of eight environment-protocol settings while reducing per-decision cost by 100-130x.
That the smoothness and uniformity regularization already present in the pretrained LeWorldModel is sufficient for a learned inverse-dynamics map to capture the planning structure that would otherwise require online search.
In regularized latent spaces of world models, planning can be amortized into a goal-conditioned inverse dynamics model that matches CEM performance at 100-130x lower per-decision cost.
Receipt and verification
| First computed | 2026-06-08T01:04:07.114185Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
52bb803ba687e781b4545b23c54d9a450e28a7cf972bee8654287832638ae294
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/KK5YAO5GQ7TYDNCULMR4KTM2IU \
| 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: 52bb803ba687e781b4545b23c54d9a450e28a7cf972bee8654287832638ae294
Canonical record JSON
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