Paper Citation Record · LEDGER

Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.05195.

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

pith.paper-citation-record.v1
2411.05195 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:31:57.503033Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T17:51:54.951706Z

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 f6e5edd4-fb82-4796-a061-7ef22e5e2c3f · inbound

Learning Visual Composition through Improved Semantic Guidance cites this paper.

Learning Visual Composition through Improved Semantic Guidance Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T11:31:57.503033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75572446-4988-44a1-9f10-22134a1a9aba · inbound

If Concept Bottlenecks are the Question, are Foundation Models the Answer? cites this paper.

If Concept Bottlenecks are the Question, are Foundation Models the Answer? Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-22T17:51:54.955071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fcf9f43a-120e-47c2-a20b-0ef7af71e6f0 · inbound

Accelerating Conditional Prompt Learning via Masked Image Modeling for Vision-Language Models cites this paper.

Accelerating Conditional Prompt Learning via Masked Image Modeling for Vision-Language Models Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T23:46:58.027310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980525a9-64b4-4034-bc9c-1b258ed6ce7a · inbound

RewardDance: Reward Scaling in Visual Generation cites this paper.

RewardDance: Reward Scaling in Visual Generation Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T20:08:58.110097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c07d3552-ce79-493c-aeb2-d11b7e1d24e0 · inbound

Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation cites this paper.

Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T21:41:29.564545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ac5c34-8a8f-4d67-b03e-e2dceb445798 · inbound

Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization cites this paper.

Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:06:03.125221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.