Pith Number

pith:YE5UQHZL

pith:2026:YE5UQHZLAOBESR4POMWRYFSOPM

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Uncertainty Quantification for Large Language Diffusion Models

Artem Shelmanov, Artem Vazhentsev, David Li, Maxim Panov, Timothy Baldwin, Vladislav Smirnov

Expected trajectory dissimilarity from the denoising process lower-bounds the masked diffusion training objective and serves as a lightweight uncertainty score for large language diffusion models.

arxiv:2605.14570 v1 · 2026-05-14 · cs.CL

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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 prove that expected trajectory dissimilarity lower bounds the masked diffusion training objective, which motivates its usage as an uncertainty score. Comprehensive experiments across three tasks, eight datasets, and two models show that our method achieves a great cost-performance trade-off: it approaches the strongest sampling-based baselines while incurring up to 100x lower computational overhead.

C2weakest assumption

The assumption that signals derived from the denoising trajectory (intermediate generations, remasking dynamics, trajectory dissimilarity) correlate with actual hallucination risk holds across the tested tasks and models and generalizes beyond them.

C3one line summary

Uncertainty signals from LLDM denoising trajectories, including a proven lower bound on the training objective, achieve near sampling-based hallucination detection at up to 100x lower cost.

References

17 extracted · 17 resolved · 1 Pith anchors

[1] Training Verifiers to Solve Math Word Problems 2021 · arXiv:2110.14168

[2] Guidelines: • The answer [N/A] means that the abstract and introduction do not include the claims made in the paper

[3] Limitations Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Y es] Justiﬁcation: Y es, the limitations of the work are discussed in Appendix E. Guideline

[4] Guidelines: • The answer [N/A] means that the paper does not include theoretical results

[5] Guidelines: • The answer [N/A] means that the paper does not include experiments

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Receipt and verification

First computed	2026-05-17T23:39:05.479000Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c13b481f2b038249478f732d1c164e7b1cb3c63fa7c7bcb5f85fc668a0e8b8ef

Aliases

arxiv: 2605.14570 · arxiv_version: 2605.14570v1 · doi: 10.48550/arxiv.2605.14570 · pith_short_12: YE5UQHZLAOBE · pith_short_16: YE5UQHZLAOBESR4P · pith_short_8: YE5UQHZL

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YE5UQHZLAOBESR4POMWRYFSOPM \
  | 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: c13b481f2b038249478f732d1c164e7b1cb3c63fa7c7bcb5f85fc668a0e8b8ef

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a743bd94e4a5a83d32ada5dadd4ef565a8eaa4dc122f2de4d65f21b1037d56ed",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-05-14T08:39:56Z",
    "title_canon_sha256": "697dca1266661c608f7bfdb751c5038d7550d04b7bae243a4cfe2aea83c4cdda"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14570",
    "kind": "arxiv",
    "version": 1
  }
}