Paper Citation Record · LEDGER

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2511.11440.

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

pith.paper-citation-record.v1
2511.11440 v3

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T22:13:55.098153Z

measured 46 of 46 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T17:30:08.755545Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T17:32:41.699954Z

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4ab86c6-7d9d-4d2d-ae2c-e476fbd476f0 · outbound

This paper cites Tal- lyqa: Answering complex counting questions.Proceedings of the AAAI Conference on Artificial Intelligence, 33(01): 8076–8084, 2019.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Tal- lyqa: Answering complex counting questions.Proceedings of the AAAI Conference on Artificial Intelligence, 33(01): 8076–8084, 2019

Reference 1

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Observation 02582e5a-e4c4-4345-8d4d-49ae9230a62f · outbound

This paper cites Lipton, and J.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Lipton, and J

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.234976Z

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Observation d8004afa-709a-4d7a-95a9-ef2437d2b00d · outbound

This paper cites [De|Re] constructing VLMs’ Reasoning in Counting.arXiv preprint arXiv:2510.19555, 2025.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation [De|Re] constructing VLMs’ Reasoning in Counting.arXiv preprint arXiv:2510.19555, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.329477Z

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Observation 49d98304-ba6b-42da-b096-ce0bf554b8db · outbound

This paper cites Spatialvlm: Endow- ing vision-language models with spatial reasoning capabili- ties.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Spatialvlm: Endow- ing vision-language models with spatial reasoning capabili- ties

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.423186Z

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Observation 98a921ef-9889-4a38-b3c0-64dc3ac7e5dd · outbound

This paper cites Are we on the right way for eval- uating large vision-language models? InAdvances in Neural Information Processing Systems, pages 27056–27087.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Are we on the right way for eval- uating large vision-language models? InAdvances in Neural Information Processing Systems, pages 27056–27087

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.510602Z

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Observation f004d32a-cb71-43e4-a4a2-45c22632dcb3 · outbound

This paper cites Why is spatial reasoning hard for VLMs? an attention mechanism perspective on focus ar- eas.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Why is spatial reasoning hard for VLMs? an attention mechanism perspective on focus ar- eas

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.593203Z

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Observation 30066842-c3b4-4b25-bd38-658ca3442d4f · outbound

This paper cites From the least to the most: Building a plug-and-play visual rea- soner via data synthesis.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation From the least to the most: Building a plug-and-play visual rea- soner via data synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.659507Z

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Observation 4a4601bb-0f81-46f6-9a32-b047c6c3691f · outbound

This paper cites Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, and Aniruddha Kembhavi.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, and Aniruddha Kembhavi

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.740903Z

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Observation 3614751c-62b0-4bd3-a8d4-47687692475d · outbound

This paper cites If CLIP could talk: Understanding vision-language model representations through their preferred concept descriptions.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation If CLIP could talk: Understanding vision-language model representations through their preferred concept descriptions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.836972Z

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Observation 720e2419-d17d-4c04-81da-ba5ae9c6d8a4 · outbound

This paper cites Hidden in plain sight: VLMs overlook their visual rep- resentations.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Hidden in plain sight: VLMs overlook their visual rep- resentations

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:49.927559Z

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Observation 1d3aaa6a-42b4-40a8-9de3-9b207f987cde · outbound

This paper cites Generate then select: Open- ended visual question answering guided by world knowl- edge.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Generate then select: Open- ended visual question answering guided by world knowl- edge

Reference 11

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Observation 316e89e8-cee1-4927-b25c-92c6f6e17315 · outbound

This paper cites Smith, Wei-Chiu Ma, and Ranjay Krishna.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Smith, Wei-Chiu Ma, and Ranjay Krishna

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:50.152594Z

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Observation 36a480b3-ba6c-4c06-a795-96bf70deea0c · outbound

This paper cites G-LLaV A: Solving geometric problem with multi-modal large language model.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation G-LLaV A: Solving geometric problem with multi-modal large language model

Reference 13

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Observation b28128d8-eb77-4543-8002-e1fe438d4e46 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:50.436120Z

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Observation 50907dbb-5bb4-4fe5-bb17-07054a6f47a1 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation LoRA: Low-rank adaptation of large language models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:50.555884Z

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Observation 6b00ee62-87f2-425e-bd20-bfb794e9f350 · outbound

This paper cites Lawrence Zitnick, and Ross Girshick.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Lawrence Zitnick, and Ross Girshick

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:50.749285Z

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Observation d022c669-b391-4b87-8b80-135ee6f0122f · outbound

This paper cites What’s “up” with vision-language models? investigating their strug- gle with spatial reasoning.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation What’s “up” with vision-language models? investigating their strug- gle with spatial reasoning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:50.897442Z

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Observation e628b165-32cd-4c05-ad72-fb8f4a817859 · outbound

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Unresolved cited work

Reference 18

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Observation 8020ff24-bcb4-46d0-8df2-9d2043873a1f · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:51.225520Z

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Observation a0e4f4e4-b149-4afb-9da9-a5d6349e233e · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.International journal of computer vision, 123(1):32–73, 2017.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Visual genome: Connecting language and vision using crowdsourced dense image annotations.International journal of computer vision, 123(1):32–73, 2017

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:51.406864Z

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Observation b953bb44-b564-4225-8daa-40dc5e8694a2 · outbound

This paper cites Enhancing vision-language com- positional understanding with multimodal synthetic data.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Enhancing vision-language com- positional understanding with multimodal synthetic data

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:51.611263Z

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Observation 17be5194-a0bf-4c10-97c9-e21c76d84c96 · outbound

This paper cites Lawrence Zitnick.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Lawrence Zitnick

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T22:13:51.817401Z

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Observation e780ebc2-2924-40b3-87b8-7aec16baef4a · outbound

This paper cites Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 23

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Observation ba066641-729c-4b3d-b6c6-854f6ebf7940 · outbound

This paper cites Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation

Reference 24

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Observation bef716b4-6525-473c-848e-9e9c7f6afce5 · outbound

This paper cites SpaRE: Enhancing spatial reasoning in vision-language models with synthetic data.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation SpaRE: Enhancing spatial reasoning in vision-language models with synthetic data

Reference 25

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Observation 62a8903a-3fed-4c1e-a9a8-2600dd6c57ac · outbound

This paper cites Teaching clip to count to ten.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Teaching clip to count to ten

Reference 26

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This paper cites Synthetic visual genome.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Synthetic visual genome

Reference 27

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Observation ddea47cf-f4be-4333-b97e-6c8581d64af2 · outbound

This paper cites Synthesize diagnose and optimize: Towards fine- grained vision-language understanding.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Synthesize diagnose and optimize: Towards fine- grained vision-language understanding

Reference 28

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Observation f79ce9f0-e1da-4f9b-8dd0-f6c83610665d · outbound

This paper cites Learning transferable visual models from natural language supervision.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Learning transferable visual models from natural language supervision

Reference 29

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Vision language models are blind

Reference 30

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation CIVET: Systematic evaluation of understanding in VLMs

Reference 31

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This paper cites Sophia Koepke, Oriol Vinyals, Cordelia Schmid, and Zeynep Akata.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Sophia Koepke, Oriol Vinyals, Cordelia Schmid, and Zeynep Akata

Reference 32

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Observation 1ef3f6a8-c342-4a3c-a0c4-6caabc9abc86 · outbound

This paper cites Forgotten polygons: Multimodal large language models are shape-blind.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Forgotten polygons: Multimodal large language models are shape-blind

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This paper cites Laion- 400m: Open dataset of clip-filtered 400 million image-text pairs.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Laion- 400m: Open dataset of clip-filtered 400 million image-text pairs

Reference 34

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Math- LLaV A: Bootstrapping mathematical reasoning for multi- modal large language models

Reference 35

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 36

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Embodied scene understanding for vi- sion language models via metavqa

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Mmmu: A massive multi-discipline multimodal understand- ing and reasoning benchmark for expert agi

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This paper cites When and why vision- language models behave like bags-of-words, and what to 10 do about it? InThe Eleventh International Conference on Learning Representations, 2023.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation When and why vision- language models behave like bags-of-words, and what to 10 do about it? InThe Eleventh International Conference on Learning Representations, 2023

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This paper cites Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? InComputer Vision – ECCV 2024, pages 169–186, Cham, 2025.

Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? InComputer Vision – ECCV 2024, pages 169–186, Cham, 2025

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation MA VIS: Mathe- matical visual instruction tuning with an automatic data en- gine

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Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation Answer with as few words as possible

Reference 42

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Reference 2023

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Pith citing papers

Observation e33b6d2c-f801-402b-be1b-d0f5f2b55709 · inbound

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision cites this paper.

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation

Reference 11

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Observation d2dfba28-d0b1-4370-91e8-61f472f0de65 · inbound

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision cites this paper.

Reference 11

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