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Paper Citation Record · LEDGER

Large Language Models Can Understanding Depth from Monocular Images

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.01133.

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

pith.paper-citation-record.v1
2409.01133 v1

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-15T14:07:39.892672Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T11:13:02.800174Z

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 714604f2-53d1-4d24-ba05-964988fa3231 · inbound

AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models cites this paper.

AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models Large Language Models Can Understanding Depth from Monocular Images

Reference 103

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:13:02.802277Z

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=pdf_text observed=2026-05-19T11:12:41.130806Z digest=sha256:48d714e1e322cd393407e005ce8d7f6b21bf53cdc7eadc0f3fa96dbfade50361

Observation 0fa5598e-b729-4188-9b37-d89559577fa9 · inbound

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP cites this paper.

A novel Framework for Open-Vocabulary Multi-Object Recognition using CLIP Large Language Models Can Understanding Depth from Monocular Images

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-15T14:07:39.892672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T14:07:39.892672Z digest=sha256:6b956e76a209e349df907f7c5eb49cb96ed19535f5ce8d70dba16fefcd79c2da