Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.02509.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:56:02.594056Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 675a2433-22fe-4987-bdc3-3a1c6198ef8e · inbound
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs cites this paper.
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T19:35:47.192556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b2f2e1e4-4ddd-4826-9dfa-24101cdcdbb6 · inbound
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems cites this paper.
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T09:22:15.976487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 89cfff04-25ca-4dec-8452-2a44660c1c06 · inbound
Cleanse: Uncertainty Estimation Approach Using Clustering-based Semantic Consistency in LLMs cites this paper.
Cleanse: Uncertainty Estimation Approach Using Clustering-based Semantic Consistency in LLMs SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:56:02.594056Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dc96d5e2-42fa-440c-97d1-446b4c32b53a · inbound
Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification cites this paper.
Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T06:31:30.611186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 53b91a37-ad03-4979-8ab5-4874da71b99b · inbound
Compared to What? Baselines and Metrics for Counterfactual Prompting cites this paper.
Compared to What? Baselines and Metrics for Counterfactual Prompting SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Reference 57
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-09T19:05:10.431926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.