Paper Citation Record · LEDGER

Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2403.20271.

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

pith.paper-citation-record.v1
2403.20271 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 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 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:55.390063Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:09:55.285599Z

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 5c953bd2-e379-4792-a718-1b9d1fd8a12d · inbound

VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding cites this paper.

VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:19:59.969643Z

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.

Observation 1ebdb2ac-b14b-4722-991f-169e278f991f · inbound

LPOI: Listwise Preference Optimization for Vision Language Models cites this paper.

LPOI: Listwise Preference Optimization for Vision Language Models Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:55.390063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8038ddba-2872-40b6-9f42-843263afc2fe · inbound

Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs cites this paper.

Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:09.279344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8aa6eeb3-2245-4d99-8f1c-4cef689c819a · inbound

RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought cites this paper.

RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:08:42.177990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 734693f4-cdbe-494d-a851-2c3d8c130a24 · inbound

Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos cites this paper.

Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T10:28:11.715662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88536864-af88-40b0-8df6-7f58519084cf · inbound

DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real World cites this paper.

DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real World Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:40.583636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13065974-bb15-4c14-9943-db6377dce38b · inbound

Finding Needles in Images: Can Multimodal LLMs Locate Fine Details? cites this paper.

Finding Needles in Images: Can Multimodal LLMs Locate Fine Details? Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T23:35:21.467830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f185046-efe2-4236-9ccb-86527044b2be · inbound

The high-speed X-ray camera on AXIS: design and performance updates cites this paper.

The high-speed X-ray camera on AXIS: design and performance updates Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T18:48:03.409499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b819030b-abb2-474b-a43c-1fdb5303f7b7 · inbound

VoCap: Video Object Captioning and Segmentation from Any Prompt cites this paper.

VoCap: Video Object Captioning and Segmentation from Any Prompt Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T14:01:17.690922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eb49cf9-3455-43e8-8fe9-ea4402bdc796 · inbound

Eevee: Towards Close-up High-resolution Video-based Virtual Try-on cites this paper.

Eevee: Towards Close-up High-resolution Video-based Virtual Try-on Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-17T06:39:10.444767Z

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.

Observation a8333f38-25bb-48dd-b407-e8f5f10b7d6e · inbound

MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition cites this paper.

MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:21:23.357910Z

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.

Observation 84cc4755-f6aa-404c-a487-402e84ec102e · inbound

VABench: A Comprehensive Benchmark for Audio-Video Generation cites this paper.

VABench: A Comprehensive Benchmark for Audio-Video Generation Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:08:43.880478Z

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.

Observation 099aca5f-e86d-4736-b73d-a26ef6ccb138 · inbound

Enhancing Foundation VLM Robustness to Missing Modality: Scalable Diffusion for Bi-directional Feature Restoration cites this paper.

Enhancing Foundation VLM Robustness to Missing Modality: Scalable Diffusion for Bi-directional Feature Restoration Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:37:37.247123Z

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.

Observation bc407873-50c7-4d54-a77b-68db0bdaf033 · inbound

E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes cites this paper.

E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T09:40:35.631188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08b66f69-779f-4a4c-84da-280aaae95099 · inbound

Less Detail, Better Answers: Degradation-Driven Prompting for VQA cites this paper.

Less Detail, Better Answers: Degradation-Driven Prompting for VQA Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:47.909302Z

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.

Observation 831e340b-8378-43ec-b7c0-8f747c970060 · inbound

LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation cites this paper.

LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 95

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:11:11.116785Z

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.

Observation b2302e69-4835-4f13-a134-b540e1896a1c · inbound

WOW-Seg: A Word-free Open World Segmentation Model cites this paper.

WOW-Seg: A Word-free Open World Segmentation Model Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:27:48.040652Z

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.

Observation ea1e2af6-8aae-4be1-b89c-5dd4ddf80e68 · inbound

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding cites this paper.

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T17:53:46.750455Z

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.

Observation 412f60c4-fc1f-48c4-9595-47af92b3b169 · inbound

From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models cites this paper.

From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:09:55.287671Z

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.

Observation 9ceaa00c-f8bb-40b8-93d5-11c989a28ae9 · inbound

Actor as Its Own Critic: Unifying Region Understanding and Localization via CycleGRPO cites this paper.

Actor as Its Own Critic: Unifying Region Understanding and Localization via CycleGRPO Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-14T04:38:05.237334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.