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pith:BE3XEHDF

pith:2026:BE3XEHDFIO333TGJSXD235QPQH
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Diffusion-Inspired Reconfiguration of Transformers for Uncertainty Calibration

Bryan Kian Hsiang Low, Manh Cuong Dao, Phi Le Nguyen, Quang Hung Pham, Thao Nguyen Truong, Trong Nghia Hoang

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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3 Author claim open · sign in to claim
4 Citations open
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Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

Diffusion-inspired probabilistic mappings enable principled uncertainty propagation through pre-trained transformer layers without degrading accuracy.

Formal links

2 machine-checked theorem links

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

arxiv: 2602.08920 · arxiv_version: 2602.08920v2 · doi: 10.48550/arxiv.2602.08920 · pith_short_12: BE3XEHDFIO33 · pith_short_16: BE3XEHDFIO333TGJ · pith_short_8: BE3XEHDF
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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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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-02-09T17:24:47Z",
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