Paper Citation Record · LEDGER

Reproducible scaling laws for contrastive language-image learning

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2212.07143.

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

pith.paper-citation-record.v1
2212.07143 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:24:42.955755Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

29
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b08436f6-1ac7-4119-939d-30cfa8f90f41 · inbound

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs cites this paper.

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs Reproducible scaling laws for contrastive language-image learning

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-13T10:42:22.444828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9715ba09-e208-4913-9dee-dacd82f9feb7 · inbound

Demystifying CLIP Data cites this paper.

Demystifying CLIP Data Reproducible scaling laws for contrastive language-image learning

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:20:20.258305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f8aaa533-26f6-4eab-9783-c38b1e9ed019 · inbound

How to Merge Your Multimodal Models Over Time? cites this paper.

How to Merge Your Multimodal Models Over Time? Reproducible scaling laws for contrastive language-image learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T19:24:42.955755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acab2e52-707d-4483-a256-c636ab96fd9b · inbound

Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs cites this paper.

Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs Reproducible scaling laws for contrastive language-image learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T04:20:51.146796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04d7566f-7a83-4a45-bb6a-58d9e052c98d · inbound

Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance cites this paper.

Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance Reproducible scaling laws for contrastive language-image learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:07.383990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ea629e6a-4d35-4f93-95f9-4b4a1c4a7486 · inbound

$A^2$: Smaller Self-Supervised ViTs Localize Better than Larger Ones cites this paper.

$A^2$: Smaller Self-Supervised ViTs Localize Better than Larger Ones Reproducible scaling laws for contrastive language-image learning

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T11:22:02.953031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a4046ef0-5b55-4d7e-bb6d-abf873858213 · inbound

Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection cites this paper.

Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection Reproducible scaling laws for contrastive language-image learning

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T07:34:21.586222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4c92cdeb-a40f-4bfe-84cb-56147902b07f · inbound

Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs cites this paper.

Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs Reproducible scaling laws for contrastive language-image learning

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T15:28:33.740990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ee5a565a-6ed5-4274-8286-a1485c55bbb5 · inbound

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling cites this paper.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Reproducible scaling laws for contrastive language-image learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T09:51:03.329640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.