pith:TN6TYKXH
Test-Time Training Done Right
Large-chunk updates during inference make test-time training efficient enough to scale nonlinear states to 40 percent of model parameters.
arxiv:2505.23884 v1 · 2025-05-29 · cs.LG · cs.CL · cs.CV
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\usepackage{pith}
\pithnumber{TN6TYKXHBTFO5YS4ZQKFT4CEP5}
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Claims
LaCT improves hardware utilization by orders of magnitude, facilitates scaling of nonlinear state size (up to 40% of model parameters), and enables 14B-parameter AR video diffusion on 56K tokens and 1M-token novel view synthesis without custom kernels.
That performing weight updates on extremely large chunks (2K–1M tokens) preserves or improves modeling quality compared with the fine-grained causal updates used in prior TTT work.
Large-chunk online updates during inference let test-time training scale state capacity to 40% of model size and handle contexts up to 1M tokens without custom kernels.
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| First computed | 2026-05-17T23:38:48.037169Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9b7d3c2ae70ccaeee25ccc1459f0447f7e2a46c7cf1ca4d52ab35e26f0bf7927
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/TN6TYKXHBTFO5YS4ZQKFT4CEP5 \
| 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: 9b7d3c2ae70ccaeee25ccc1459f0447f7e2a46c7cf1ca4d52ab35e26f0bf7927
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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