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Paper Citation Record · LEDGER

Synthetic Video Enhances Physical Fidelity in Video Synthesis

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.20822.

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

pith.paper-citation-record.v1
2503.20822 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:53:00.006415Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T00:56:24.880471Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ab7a1c28-9891-4c5d-bca6-60ad4c59baa2 · inbound

Generative Physical AI in Vision: A Survey cites this paper.

Generative Physical AI in Vision: A Survey Synthetic Video Enhances Physical Fidelity in Video Synthesis

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-10T18:53:00.006415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:53:00.006415Z digest=sha256:3adfbda8c06237baa162b913630bc743024fb337e73d1b331a4f113bd618d1b9

Observation ef7937bb-5a5c-4068-bd49-b5477674f288 · inbound

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation cites this paper.

Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation Synthetic Video Enhances Physical Fidelity in Video Synthesis

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-05T20:20:26.433306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:20:26.433306Z digest=sha256:8240fcf642328449fb59cfb030a49370cc2c9fe7f51072d8c57c8cca2275fd71

Observation 859bc576-4e8a-4a9a-9a69-8cbd359a2034 · inbound

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms cites this paper.

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms Synthetic Video Enhances Physical Fidelity in Video Synthesis

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-05T20:17:52.808648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:17:52.808648Z digest=sha256:2f16bd4f8c0ddc6ee210f3bc119d2a5d4cef8e20afef94dec8c15ec323371f2d

Observation 678619fc-1bfe-4919-bde2-5c517f6e898d · inbound

CustomX: Unified Character, Action, and Scene Customization in Video World Models cites this paper.

CustomX: Unified Character, Action, and Scene Customization in Video World Models Synthetic Video Enhances Physical Fidelity in Video Synthesis

Reference 101

Resolution
unresolved
no resolver link, observed 2026-08-03T15:29:01.336195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:29:01.336195Z digest=sha256:8f9be2fef3a2f4e52db9d9980336f5765a39e6f52db42c37b408aa93169848ff

Observation db20ed10-3bf7-4a74-9e63-39ef519965c5 · inbound

TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization cites this paper.

TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization Synthetic Video Enhances Physical Fidelity in Video Synthesis

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-07-02T00:56:24.882461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-28T13:07:53.001390Z digest=sha256:f1af05acfaec14f45f06fe6cfd66a9a168bc67b62ff22250566a368bc7e2d0f8