pith:E25GRCME
Divide and Conquer: Decoupled Representation Alignment for Multimodal World Models
Decoupling modality features in a diffusion model allows each to align with a separate foundation model expert for improved multi-modal video generation.
arxiv:2605.01896 v2 · 2026-05-03 · cs.CV
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Claims
This design enables joint optimization, fully exploiting priors from multiple foundation models. Extensive experiments demonstrate that our method significantly outperforms baselines in visual quality and long-term consistency.
Foundation models trained on different modality spaces naturally capture distinct domain-specific priors, acting as complementary experts, and that the proposed decoupling regularization will maintain this complementarity without introducing new conflicts during joint optimization.
M²-REPA decouples modality-specific features inside a diffusion model and aligns each to its matching expert foundation model via an alignment loss plus a decoupling regularizer, yielding better visual quality and long-term consistency in multi-modal video generation.
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| First computed | 2026-07-02T01:17:32.037792Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
26ba688984aa2b8559048577f1a8c80bf6317aec6c146228e1bf54ec6a9a7857
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/E25GRCMEVIVYKWIEQV37DKGIBP \
| 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: 26ba688984aa2b8559048577f1a8c80bf6317aec6c146228e1bf54ec6a9a7857
Canonical record JSON
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