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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 13 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 2026-08-13T06:32:02.005865+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=sha256:6b1252bd8509b7ca55a6cf1724cf383ca481a5d9b2878f4ed07c23c54e4453f4

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=sha256:4dff4223f7ebbb118c1f91969e44dfe585942491efb5c90ebd69306d6eaf0e7e

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=sha256:457b9bc1a5a2d93c117869815e6b24e42900bc7f1076d300f241fdfd94201b41

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-reported events for the cited work

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source=arxiv_source observed=2026-08-07T15:02:25.132191Z digest=sha256:610c801b4bb2aa1d435a998109ee6dd9b4c3be2bb7dd925594c9cc494c0c1042

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=sha256:7b7ce5ba63379d59904183f91208b589fbe323e711f8bae9cff40403a430ee87

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T15:02:25.246792Z digest=sha256:46e14a78684d4ad4c805707275e3d9ffbc55f65d46492c2d3bcee5cca5f17015

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:e543ff1b0c27fdef142683241491ba309f08fc3277511977622307d6e319ea65

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:f6a694021593e391a6072acdc55a39efdfa4e045a179d174dc4078b0a8ec8973

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=sha256:258b204d1860c8ab5e8e3a1af02a8543be7f8eb1936f5ae17716e9c5d7091e9a

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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source=arxiv_source observed=2026-08-07T15:02:25.691311Z digest=sha256:dadc9884b97caba2b78241a89ac7a60a04051b66ad9e98b552f4b10240dce907

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:02:25.738836Z digest=sha256:934924154bb03b3a7309344501a4dfdf50567db755f41ae58ac516c424ec97bc

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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source=arxiv_source observed=2026-08-07T15:02:25.841387Z digest=sha256:3960d98adbe697167505899f096839bed1c6615366c1027e216774a4cda63077

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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source=arxiv_source observed=2026-08-07T15:02:25.944057Z digest=sha256:9fa100badc2f78dd47d96c061b68d994e7d62751a5eb5e5acf59ca1e2dfc0bb9

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=sha256:76dd6d196adb861c94ac6bb1b2aba99e64164301d32a9b4d8a0e0cac5217b732

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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raw_fallback, observed 2026-08-07T15:02:28.503460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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=sha256:8cbc2a7cf880382cfe635ae994177c02e3f0cfb12607df1eb05adf10900af235

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 2026-08-13T06:32:02.005865+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=sha256:20149c3fa3ec11f805aa586ab1d7460562a9185f4fa7f979a0bc200ae53d0284

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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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:d8c7b7a1224456463d5f05d247d90423af40b45361823d1c0369c0e307a4aac2

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:ac712eae2f89a7e2fbf8765886f8d3fcb6d0efd95acad23c7184a91f3fc69add

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=sha256:4a9dbba4633bf514bb0cf0107496529a2ddd8a2cde38e5e0ca5fba7eb691d713

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=sha256:50ed9da84b7321409dc6590b318db477c28ce16ebb67b03a765cc77c8f00eb5c

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.

source=arxiv_source observed=2026-08-07T15:02:26.931367Z digest=sha256:54e3d2460fafc4486bb76395c67cb56f8bcc6bf698f62eb38c05ef6bc607f46c

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:02:27.056855Z digest=sha256:147785d10f5ffba1443e87cbec6a9c30c33ba9b65ce608452a6098b9c0a270bf

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:fff9c73a158edc9f48c5b75aa3128f80ab8905995e64c636591fa2036ef1fa82

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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source=arxiv_source observed=2026-08-07T15:02:27.349532Z digest=sha256:1f7404bc2a9d71a3ef7f0aae56757e9e1b2e5a991a406177926ca41a2bcd21b1

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:a93a495a7d661296fd9e32c657c2054e18249ca1b8cf414be6484f558ef2c227

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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verified exact
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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-09T21:32:08.503584Z digest=sha256:89faf1f17bc4d3ea7b3faa3d62eb148d868fc620ffd07c2b848256087f3a29a4