Pith. sign in

Paper Citation Record · LEDGER

Between Copyright and Computer Science: The Law and Ethics of Generative AI

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.14653.

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

pith.paper-citation-record.v1
2403.14653 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:09:32.607386Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T08:30:31.481916Z

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 25bb999b-3a83-48b4-bf33-739e97dd7df3 · inbound

XAttnMark: Learning Robust Audio Watermarking with Cross-Attention cites this paper.

XAttnMark: Learning Robust Audio Watermarking with Cross-Attention Between Copyright and Computer Science: The Law and Ethics of Generative AI

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:30:31.484978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-25T08:30:15.011210Z digest=sha256:bb3672d71526417b919da982276817e50a5c312e493a99e6677ca92f1002dacb

Observation 93213459-0c2c-4144-9462-c5bb7458b81a · inbound

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI cites this paper.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Between Copyright and Computer Science: The Law and Ethics of Generative AI

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T20:09:32.607386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:09:32.607386Z digest=sha256:a8b82c7857766b127fb01af17ac7aa97a89e6b4f3edc954791bf3f2453dd3959