pith:BE3XEHDF
Diffusion-Inspired Reconfiguration of Transformers for Uncertainty Calibration
Modeling each transformer block as a probabilistic mapping creates a diffusion-like path that propagates representation uncertainty without changing predictions.
arxiv:2602.08920 v2 · 2026-02-09 · cs.LG
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Claims
Composing these probabilistic mappings reveals a probability path that mimics the structure of a diffusion process, transporting data mass from the input distribution to the pre-trained feature distribution. This probability path can then be recompiled on a diffusion process with a unified transition model to enable principled propagation of representation uncertainty throughout the pre-trained model's architecture while maintaining its original predictive performance.
That modeling each feature transformation block as a probabilistic mapping accurately captures and propagates representation uncertainty without introducing systematic biases or changing the model's learned behavior in unintended ways.
Diffusion-inspired probabilistic mappings enable principled uncertainty propagation through pre-trained transformer layers without degrading accuracy.
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Receipt and verification
| First computed | 2026-05-18T03:09:23.692577Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0937721c6543b7bdccc995c7adf60f81f98e9fc1b80eb06c5ff04ffcaeacc4cd
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BE3XEHDFIO333TGJSXD235QPQH \
| 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: 0937721c6543b7bdccc995c7adf60f81f98e9fc1b80eb06c5ff04ffcaeacc4cd
Canonical record JSON
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