Paper Citation Record · LEDGER

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models

As of 5 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2502.02970.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.02970 v5

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T04:27:59.317818Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T18:17:19.224081Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-02T23:17:30.038612Z

Reference resolution

17 of 17 outbound references displayed

verified exact9
verified fuzzy6
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38899e67-6437-4de5-a5ca-73c8c1355994 · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models MiniLLM: On-Policy Distillation of Large Language Models

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:32:34.104190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 32bd76dd-b6f7-4929-bc4c-c2f67ca14f06 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:32:34.073158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 12a9bdc9-d4b6-4ef2-a023-323306ff4c50 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models Distilling the Knowledge in a Neural Network

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:32:34.098003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c4fd833b-95b9-46e3-905e-64d680ec2012 · outbound

This paper cites BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:34.091964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1452674c-96c5-456b-998f-a4fc73d4b3f0 · outbound

This paper cites DeepSeek-V3 Technical Report.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models DeepSeek-V3 Technical Report

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:32:34.085554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0f976c4b-981b-4d12-80a9-d981826a6eb3 · outbound

This paper cites Datasets for Large Language Models: A Comprehensive Survey.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models Datasets for Large Language Models: A Comprehensive Survey

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:34.080255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a8167720-6f32-4f08-a8fe-57a3f9f7af08 · outbound

This paper cites A Survey on Transformer Compression.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models A Survey on Transformer Compression

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:34.067027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dd68191a-b4c2-4e03-8d14-b1e05f18a255 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models LLaMA: Open and Efficient Foundation Language Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:32:34.054175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 67dc61ae-a9f6-4540-bc8d-51cef99ab2a1 · outbound

This paper cites Adversarially Contrastive Estimation of Conditional Neural Processes.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models Adversarially Contrastive Estimation of Conditional Neural Processes

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:34.060384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 11b014e8-41ad-4c07-8c4a-b1f86183b61c · outbound

This paper cites MMD finds common application in areas such as domain adaptation (Chi et al., 2021; Jiang et al., 2023; Zheng et al.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models MMD finds common application in areas such as domain adaptation (Chi et al., 2021; Jiang et al., 2023; Zheng et al

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.863094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ef00a3c1-3f4e-4f66-9bc5-d3d263c13959 · outbound

This paper cites MMD and related techniques have been extensively adopted in real-world applications, including healthcare (Guo et al., 2022; Jiang et al., 2016; Zhong et al.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models MMD and related techniques have been extensively adopted in real-world applications, including healthcare (Guo et al., 2022; Jiang et al., 2016; Zhong et al

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.854450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9c176afe-0e30-433e-babe-aa9f6b98f14f · outbound

This paper cites In this paper, these concepts are used in quantifying the distributional differences in Section 4 and Section.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models In this paper, these concepts are used in quantifying the distributional differences in Section 4 and Section

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.860203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bd7dce9a-0a70-4bee-8a1a-aa7c47d286f3 · outbound

This paper cites Maximum Mean Discrepancy (MMD), proposed by Gretton et al.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models Maximum Mean Discrepancy (MMD), proposed by Gretton et al

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.857412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e8f7cc25-9340-4240-b785-19151511cc21 · outbound

This paper cites The MMD can then be computed in this learned feature space 𝒵.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models The MMD can then be computed in this learned feature space 𝒵

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.851493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1caf595b-fed6-49ac-9b71-bc11e858ed51 · outbound

This paper cites an unresolved cited work.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-05-23T06:07:38.844411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f8b99cf8-1cc1-4977-b17c-90000164480c · outbound

This paper cites See detailed setup of victim models inApp.A.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models See detailed setup of victim models inApp.A

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.847796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d65ec145-6d1c-4731-9e8d-d8d90a69f4d8 · outbound

This paper cites an unresolved cited work.

Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-05-23T06:07:38.841327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation 3120506b-24b3-4f35-8ef0-6f3a4402d4ab · inbound

LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry cites this paper.

LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models

Reference 75

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T23:17:30.039959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.