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Paper Citation Record · LEDGER

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2505.16579.

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

pith.paper-citation-record.v1
2505.16579 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:02:27.579774Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T21:32:08.503584Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T14:36:05.322735Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d392d12d-df48-447d-b2c8-55afe1872500 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:02:24.783223Z digest=sha256:2956255ed8620e83306117e4449c2c728dcc1debbf24356c94bb7e8126e0e2bd

Observation d369d101-b043-43a5-ac8e-35b385e3d5bc · outbound

This paper cites Qwen2.5-VL Technical Report.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Qwen2.5-VL Technical Report

Reference 2

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source=arxiv_source observed=2026-08-07T15:02:24.843868Z digest=sha256:f3c4d53ccc601250541afcbeafd1840a57f077dfca13c5116013fe526311a50e

Observation a768fc52-f4a4-45e4-8ead-41ade8a9bbb9 · outbound

This paper cites M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought

Reference 3

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source=arxiv_source observed=2026-08-07T15:02:24.955656Z digest=sha256:90f43eadb8cfb4834a454b63054f86f80f80d5e5e7e25109427914cfcab1432f

Observation 724ccaf1-5152-4020-b225-fbaa6e536ca7 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 4

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source=arxiv_source observed=2026-08-07T15:02:25.132191Z digest=sha256:e9ad740c8c49aa94f334704a9dd1d32af11a8c5b78f666cd014f3708d677205f

Observation 4e7f2367-3101-4041-9abb-96fe64d90401 · outbound

This paper cites Interleaved-Modal Chain-of-Thought.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Interleaved-Modal Chain-of-Thought

Reference 5

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source=arxiv_source observed=2026-08-07T15:02:25.200640Z digest=sha256:b06899c230375553789f78838b8a771cec547b04dd3db4bb9a30ef727ce0ad8e

Observation 9f366451-7cae-4ceb-860e-2eb4ee718d4e · outbound

This paper cites The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning

Reference 6

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local_arxiv, observed 2026-08-07T15:02:28.069695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e7c8bc35-93ae-49e7-8dd3-60f36a5cfcb2 · outbound

This paper cites MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency

Reference 7

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source=arxiv_source observed=2026-08-07T15:02:25.409128Z digest=sha256:e4ce921a33193081edd0ec320e944a5d0026607b82c31a0bf129cb76d8a8ae97

Observation 904ec99a-5f78-4d84-8995-bd187d72ae45 · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning LLaVA-OneVision: Easy Visual Task Transfer

Reference 8

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source=arxiv_source observed=2026-08-07T15:02:25.544884Z digest=sha256:d2d3f82aad0ee5dfc188c4479938d3bb87e15e03eef2bbb563f7606d2b23e15e

Observation 2d134f9e-5c17-4b86-9d2c-5ff156aa1530 · outbound

This paper cites Imagine while Reasoning in Space: Multimodal Visualization-of-Thought.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Imagine while Reasoning in Space: Multimodal Visualization-of-Thought

Reference 9

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source=arxiv_source observed=2026-08-07T15:02:25.601130Z digest=sha256:67e0be9ffacb2ce02e9c2fa5cbb3b9c14d1ef3823dec35fc2037489afbc5924a

Observation 760a5f88-7882-4a49-ba54-e8a8f01cc2d8 · outbound

This paper cites an unresolved cited work.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Unresolved cited work

Reference 10

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Observation c39c6697-9f26-4516-900b-2db79dd05e48 · outbound

This paper cites Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next Paradigm.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next Paradigm

Reference 11

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source=arxiv_source observed=2026-08-07T15:02:25.738836Z digest=sha256:fb092a4ed23ed1ed2789c29d41772c276e920fed516de53668c2970ef6654b3b

Observation 65a724fb-6abd-4279-8576-c22f418abf0d · outbound

This paper cites NVILA: Efficient Frontier Visual Language Models.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning NVILA: Efficient Frontier Visual Language Models

Reference 12

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Observation 20fc7cfd-c3b1-4448-9b4a-6f89d7d9f9c1 · outbound

This paper cites MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Reference 13

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Observation 122c7166-4311-4d08-a4a3-f36094e61cd0 · outbound

This paper cites Compositional Chain-of-Thought Prompting for Large Multimodal Models.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Compositional Chain-of-Thought Prompting for Large Multimodal Models

Reference 14

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source=arxiv_source observed=2026-08-07T15:02:26.083572Z digest=sha256:f559799c2188a983c1c09661e4a672988bcff06879ba67233e5dcabfe1e0af64

Observation 2f609678-7fec-42bd-9efb-9440d1536144 · outbound

This paper cites an unresolved cited work.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Unresolved cited work

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 5c8956ee-8a8d-4793-8f62-05ccc2f48825 · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 16

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source=arxiv_source observed=2026-08-07T15:02:26.254694Z digest=sha256:9d026e0591b43a6ccef636f2ce162be1d74d347c4b5216b0202eaa8389b5221c

Observation 3a676439-2414-4e49-95cd-ea8f163b8edd · outbound

This paper cites an unresolved cited work.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Unresolved cited work

Reference 17

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b695ae8d-aa12-48c9-a326-f4abd40500b4 · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 18

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source=arxiv_source observed=2026-08-07T15:02:26.398066Z digest=sha256:b20c6607fc898d63ff7020f1e1dfbc42b90f0b472c92742f36249eb972220840

Observation ce0a4132-05fa-4f6b-81e4-15ea8a5f2294 · outbound

This paper cites Qwen2.5-Omni Technical Report.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Qwen2.5-Omni Technical Report

Reference 19

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source=arxiv_source observed=2026-08-07T15:02:26.475930Z digest=sha256:5c85de1f35f0a55bf9dcfc27680b06c077a087337596cab9522dab7183c55a3e

Observation 259f02f7-c6b6-437b-b6b3-bbfaca65695f · outbound

This paper cites The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision).

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

Reference 20

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source=arxiv_source observed=2026-08-07T15:02:26.564151Z digest=sha256:8d9d8ab8525bc0235329dfbebbe887b0590e57f9511fdd0aca7ffd600702ac7e

Observation 18c2e927-af90-4a3f-8cb7-9d6565d0d76e · outbound

This paper cites MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

Reference 21

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source=arxiv_source observed=2026-08-07T15:02:26.636292Z digest=sha256:64c1c81cd7ac648039eb8bad797d73827083160163aee91571b631935a5f76b6

Observation 35bdbbd9-123a-4424-8122-d3144942bcae · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 22

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source=arxiv_source observed=2026-08-07T15:02:26.697184Z digest=sha256:713a10d0084c7040955a2fd21d1a56955662acbf07a9fd3b0dbf9d0c52dd3e83

Observation 565bbe37-6430-40e2-af36-f08aad36afa9 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 23

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source=arxiv_source observed=2026-08-07T15:02:26.814318Z digest=sha256:7bf09217552776103427b37f3ffc6151dd9e6ac711b1ef8744265d3da7009a1f

Observation 78815e43-b6a4-4974-be74-be751308dd65 · outbound

This paper cites an unresolved cited work.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning Unresolved cited work

Reference 24

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Unavailable: canonical work link unavailable.

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Observation 215337b5-4fd6-4475-95f7-3aba6a64c905 · outbound

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

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

Reference 25

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source=arxiv_source observed=2026-08-07T15:02:27.056855Z digest=sha256:c2da917e277f6f1e569023cee049ddaf439b0652c8e3f455aefec0b3553a6435

Observation 4887c9c4-572d-4dee-8cd3-c88ea7972825 · outbound

This paper cites DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models

Reference 26

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source=arxiv_source observed=2026-08-07T15:02:27.176868Z digest=sha256:2c5acd48fcf987089e6a39108c37b3ae9403a1a2256e80039bf151225f47a15c

Observation 75822fd1-1674-4244-a18f-b958e6bb4628 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 27

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Observation f22aed27-8280-444e-becf-9813a3e5a6f0 · outbound

This paper cites online" 'onlinestring :=.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning online" 'onlinestring :=

Reference 28

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Unavailable: canonical work link unavailable.

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Observation 548b94c8-3f8f-4a2e-8a38-ceb2116087ed · outbound

This paper cites write newline.

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning write newline

Reference 29

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source=arxiv_source observed=2026-08-07T15:02:27.579774Z digest=sha256:a7c6573c931c3a14994a1223449ca83be14c3da2c01631d592bf734f64ebe74d

Pith citing papers

Observation a3be3925-d3a2-4896-8e84-2d466f961232 · inbound

SketchVLM: Vision language models can annotate images to explain thoughts and guide users cites this paper.

SketchVLM: Vision language models can annotate images to explain thoughts and guide users Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning

Reference 33

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arxiv_id, observed 2026-05-11T14:36:05.326174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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