Pith Number

pith:JU2PD27V

pith:2026:JU2PD27VDSAIOXBEGUCX7RRUZ7

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CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision

Liang Qian, Li-Ta Hsu, Penggao Yan, Penghui Xu

Training GNSS factor graphs end-to-end with scoring rules on output covariance produces more credible uncertainty estimates while also sharpening position fixes in urban canyons.

arxiv:2605.06100 v2 · 2026-05-07 · eess.SP · cs.AI · cs.LG · cs.RO

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Claims

C1strongest claim

CDFGO makes covariance credibility an explicit training target; the WGN predicts per-satellite weights, the differentiable Gauss-Newton solver maps them to position and posterior covariance, and proper scoring rules (NLL, ES, combination) supervise the East-North predictive distribution end-to-end, producing consistent gains in uncertainty credibility and some accuracy improvements on UrbanNav scenes.

C2weakest assumption

That supervising the East-North predictive distribution with NLL and ES on the output of the differentiable Gauss-Newton solver will produce a posterior covariance whose credibility generalizes beyond the training scenes and whose shape is meaningfully improved rather than merely fitted to the scoring rules.

C3one line summary

CredibleDFGO adds explicit supervision of covariance credibility to differentiable factor graph optimization for GNSS by using proper scoring rules on the predictive distribution, yielding more trustworthy uncertainties on urban test scenes.

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1 paper in Pith

CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision

Receipt and verification

First computed	2026-06-11T01:09:37.346855Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4d34f1ebf51c80875c2435057fc634cfce9aa7a27476a4a3f5c96428f834049b

Aliases

arxiv: 2605.06100 · arxiv_version: 2605.06100v2 · doi: 10.48550/arxiv.2605.06100 · pith_short_12: JU2PD27VDSAI · pith_short_16: JU2PD27VDSAIOXBE · pith_short_8: JU2PD27V

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JU2PD27VDSAIOXBEGUCX7RRUZ7 \
  | 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: 4d34f1ebf51c80875c2435057fc634cfce9aa7a27476a4a3f5c96428f834049b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "35a8a2fe58c05b50c966e73646082c50a91aec669f81f88715d5fa9949131434",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG",
      "cs.RO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-07T12:16:31Z",
    "title_canon_sha256": "3981e9bf705c8f51a0b444d7326314a32e730e499ab74fa1da36efac21efd24b"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 2
  }
}