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Paper Citation Record · LEDGER

A Sparsity Principle for Partially Observable Causal Representation Learning

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.08335.

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

pith.paper-citation-record.v1
2403.08335 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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T14:25:04.811472Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:59:37.876786Z

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 cd07aab6-119b-4990-b1a2-2d2f5f161b6e · inbound

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability cites this paper.

Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability A Sparsity Principle for Partially Observable Causal Representation Learning

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:59:37.879448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-06-26T15:00:18.283411Z digest=sha256:a7855fc0acb7d009b519137c359c809c78322c80b65f2b597988caa054d5ee47

Observation 38c626a2-ca7e-4deb-9524-2f88fb4893b2 · inbound

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment cites this paper.

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment A Sparsity Principle for Partially Observable Causal Representation Learning

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-07-02T14:27:03.031386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-07-02T14:25:04.811472Z digest=sha256:6b793fc4a1f0de9792e687e987bab2aa2b4b19b43b46f6be7fce3769a149c868