Pith Number

pith:NIRGHDTQ

pith:2026:NIRGHDTQJFPAHKIG73CEXACGY6

not attested not anchored not stored refs pending

Generative Augmented Inference

Cheng Lu, Dennis J. Zhang, Heng Zhang, Mengxin Wang

GAI uses an orthogonal moment construction to incorporate LLM-generated outputs for consistent estimation and valid inference on human-labeled outcomes with a nonparametric relationship.

arxiv:2604.14575 v2 · 2026-04-16 · cs.LG · cs.AI · stat.ME · stat.ML

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Claims

C1strongest claim

GAI uses an orthogonal moment construction that enables consistent estimation and valid inference with flexible, nonparametric relationship between LLM-generated outputs and human labels. We establish asymptotic normality and show a 'safe default' property: relative to human-data-only estimators, GAI weakly improves estimation efficiency under arbitrary auxiliary signals and yields strict gains whenever the auxiliary information is predictive.

C2weakest assumption

The auxiliary AI signals are generated independently of the human labeling process in a way that permits the orthogonal moment conditions to identify the target parameters without additional parametric restrictions on the relationship between AI outputs and human labels.

C3one line summary

GAI uses orthogonal moment conditions to integrate arbitrary AI-generated auxiliary data into human-label models, delivering consistent estimates, asymptotic normality, and a safe-default efficiency improvement over human-data-only methods.

Receipt and verification

First computed	2026-06-04T00:06:50.836025Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6a22638e70495e03a906fec44b8046c7bdd8c27187a2836faeac1756c79bf6ca

Aliases

arxiv: 2604.14575 · arxiv_version: 2604.14575v2 · doi: 10.48550/arxiv.2604.14575 · pith_short_12: NIRGHDTQJFPA · pith_short_16: NIRGHDTQJFPAHKIG · pith_short_8: NIRGHDTQ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NIRGHDTQJFPAHKIG73CEXACGY6 \
  | 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: 6a22638e70495e03a906fec44b8046c7bdd8c27187a2836faeac1756c79bf6ca

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "1dcb81c04289b4a6085a03dbb775466610f2d6b6fc9a22b7fae5fea86d019eef",
    "cross_cats_sorted": [
      "cs.AI",
      "stat.ME",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-16T03:10:37Z",
    "title_canon_sha256": "d9c619a92c0d53f6b5a9d04053daa8fd2309908cc50408b2cee96620c744fc09"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.14575",
    "kind": "arxiv",
    "version": 2
  }
}