Pith Number

pith:REGQYJ4K

pith:2026:REGQYJ4KZCCGLDPRXANMUJYKXF

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Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment

Dongwan Kang, Hwanil Choi, Jaehoon Lee, Jun Seo, Minjae Kim, Seunghan Lee, Soonyoung Lee, Sungdong Yoo, Tae Yoon Lim, Wonbin Ahn

DistPFN rescales TabPFN output probabilities at test time to counteract label shift by downweighting the training class prior.

arxiv:2605.04363 v2 · 2026-05-06 · cs.LG · cs.AI

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We propose DistPFN, the first test-time posterior adjustment method designed for tabular foundation models... demonstrating substantial improvements for various TabPFN-based models in classification tasks under label shift, while maintaining strong performance in standard settings without label shift.

C2weakest assumption

That rescaling the model's output probabilities by downweighting the training prior (and optionally applying adaptive temperature) will reliably recover a better posterior under label shift without introducing new errors or requiring knowledge of the true test prior.

C3one line summary

DistPFN is a test-time posterior adjustment that rescales TabPFN class probabilities to reduce overfitting to the training class distribution under label shift.

Formal links

3 machine-checked theorem links

Receipt and verification

First computed	2026-05-26T01:03:32.218964Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

890d0c278ac884658df1b81aca270ab95be0b71f6ed3580fe9102e39491105ae

Aliases

arxiv: 2605.04363 · arxiv_version: 2605.04363v2 · doi: 10.48550/arxiv.2605.04363 · pith_short_12: REGQYJ4KZCCG · pith_short_16: REGQYJ4KZCCGLDPR · pith_short_8: REGQYJ4K

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/REGQYJ4KZCCGLDPRXANMUJYKXF \
  | 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: 890d0c278ac884658df1b81aca270ab95be0b71f6ed3580fe9102e39491105ae

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c109a16d5c62da25cd21e50e7a7fd4033cd71f5d25383bdce86d68e4b0815685",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-06T00:01:47Z",
    "title_canon_sha256": "670a581c6ecc480b0819f7ebc4eeed39c1c4e3f55755e8e3de1cc0dcb5a27e13"
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  "source": {
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    "kind": "arxiv",
    "version": 2
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}