Paper Citation Record · LEDGER

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation

As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2507.02859.

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

pith.paper-citation-record.v1
2507.02859 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:24:57.154109Z

measured 41 of 41 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T10:06:52.822171Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:46:55.443273Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy17
unresolved20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dcf504e3-25e2-4fb6-b3f4-b1afc63c03a0 · outbound

This paper cites Lion: Empowering multimodal large language model with dual-level visual knowledge.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Lion: Empowering multimodal large language model with dual-level visual knowledge

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T20:24:56.982611Z

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Observation b9b49cf4-48b3-4927-bedb-62f79bc4c293 · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T20:24:56.987451Z

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Observation fddf9d81-e69c-41bc-8704-d7293b380278 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Training Verifiers to Solve Math Word Problems

Reference 3

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Observation fde16b29-bd6f-49a1-9fc3-c35633c2d7c0 · outbound

This paper cites The Llama 3 Herd of Models.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation The Llama 3 Herd of Models

Reference 4

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Observation 337842db-ae19-48bd-b2cc-4701c88c8015 · outbound

This paper cites ChartLlama: A Multimodal LLM for Chart Understanding and Generation.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation ChartLlama: A Multimodal LLM for Chart Understanding and Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:24:57.001879Z

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Observation c84e23e9-1b37-4075-a4e2-ba347837b1f8 · outbound

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

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Lora: Low-rank adaptation of large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.717781Z

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.

Observation 89640aa1-16c9-4a21-981c-4d7c069ee456 · outbound

This paper cites Icdar2019 compe- tition on scanned receipt ocr and information extraction.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Icdar2019 compe- tition on scanned receipt ocr and information extraction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:24:57.010697Z

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Observation 41ccbb43-9ec3-4e91-bcb1-ccd0d4e2fdf1 · outbound

This paper cites Dvqa: Understanding data visualizations via ques- tion answering.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Dvqa: Understanding data visualizations via ques- tion answering

Reference 8

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Observation 75e04989-60eb-42bd-990c-34ef9714ebcb · outbound

This paper cites Large language models are zero-shot reasoners.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Large language models are zero-shot reasoners

Reference 9

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Observation 9b952664-fbd5-4b9f-b041-c23bd4d6d716 · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Visual genome: Connecting language and vision using crowdsourced dense image annotations

Reference 10

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Observation 70ba3b26-17d3-41d2-8670-94555f69bacc · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 11

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Observation e2f59f45-9306-4a10-ae4f-c6dca2908af5 · outbound

This paper cites Deductive verification of chain-of-thought reasoning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Deductive verification of chain-of-thought reasoning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.656097Z

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Observation eeefdf7a-eee3-4929-9f3c-0eb9cc76fd04 · outbound

This paper cites Improved baselines with visual instruction tuning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Improved baselines with visual instruction tuning

Reference 13

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Observation f691e5eb-9093-4fec-abe8-181e7349ec9e · outbound

This paper cites Visual instruction tuning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Visual instruction tuning

Reference 14

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Observation 8d73a0a5-26ab-4176-a6f0-1edefb03280d · outbound

This paper cites Nltk: The natural language toolkit.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Nltk: The natural language toolkit

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.617121Z

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Observation 730f94d4-a500-453d-b600-77ff873e777c · outbound

This paper cites Decoupled weight decay regularization, 2019.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Decoupled weight decay regularization, 2019

Reference 16

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Observation f67e04b2-c603-40db-b79a-40384cc05c33 · outbound

This paper cites Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.594983Z

Source-reported events for the cited work

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Observation 295cb5a7-f4bc-4a59-97ab-171cb26625f2 · outbound

This paper cites Chartqa: A benchmark for question an- swering about charts with visual and logical reasoning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Chartqa: A benchmark for question an- swering about charts with visual and logical reasoning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.580807Z

Source-reported events for the cited work

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Observation f3f7c609-a6df-4643-a018-ee3e45b957f7 · outbound

This paper cites ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild

Reference 19

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Observation 196330d8-89f1-494f-bb63-b6a766836d71 · outbound

This paper cites ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning

Reference 20

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Observation 5684f8a7-b8f4-4511-a2fa-e21875e76203 · outbound

This paper cites Selfcheck: Using llms to zero-shot check their own step-by-step reason- ing.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Selfcheck: Using llms to zero-shot check their own step-by-step reason- ing

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.567212Z

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Observation 7c4cc44b-90d6-4fef-946c-a6aa0c2d07da · outbound

This paper cites Im2text: Describing images using 1 million captioned pho- tographs.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Im2text: Describing images using 1 million captioned pho- tographs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.552516Z

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Observation 865f1f13-391c-44c2-8d01-0426461d180b · outbound

This paper cites Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models

Reference 23

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Observation a3bd51e3-7e11-445c-b490-77522bcb375e · outbound

This paper cites Aligning large and small language models via chain-of-thought reasoning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Aligning large and small language models via chain-of-thought reasoning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.530060Z

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Observation de6b8527-f939-4f29-97c2-c8f351b2d215 · outbound

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

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Laion-400m: Open dataset of clip-filtered 400 million image-text pairs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.516556Z

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Observation 6127810b-2b5a-498c-bfd3-905d4efbf858 · outbound

This paper cites Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.503066Z

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Observation 3bcdec76-d803-4e29-ad25-d2a84edfcd44 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.488546Z

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Observation 181ad49c-580b-4edb-9735-fbf457ff94d6 · outbound

This paper cites Mome: Mixture of multimodal experts for generalist multimodal large language models.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Mome: Mixture of multimodal experts for generalist multimodal large language models

Reference 28

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Observation 3e38e282-e2d4-488f-aaf8-06f6ad2db1ce · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large lan- guage models.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Chain-of-thought prompting elicits reasoning in large lan- guage models

Reference 29

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Observation 33aafc2c-50ed-4f2a-8985-4d80ee31dcc9 · outbound

This paper cites Grounded Chain-of-Thought for Multimodal Large Language Models.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Grounded Chain-of-Thought for Multimodal Large Language Models

Reference 30

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Observation 4be37be1-5449-44a0-a5b5-3afe5f5a0f82 · outbound

This paper cites Visionary-r1: Mitigating shortcuts in vi- sual reasoning with reinforcement learning.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Visionary-r1: Mitigating shortcuts in vi- sual reasoning with reinforcement learning

Reference 31

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This paper cites Falcon: Resolv- ing visual redundancy and fragmentation in high-resolution multimodal large language models via visual registers.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Falcon: Resolv- ing visual redundancy and fragmentation in high-resolution multimodal large language models via visual registers

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.456072Z

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Observation 8b606a23-4068-4979-ab81-d52df5b795f6 · outbound

This paper cites Tat-qa: A question answering benchmark on a hybrid of tab- ular and textual content in finance.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Tat-qa: A question answering benchmark on a hybrid of tab- ular and textual content in finance

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.442042Z

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Observation d1ea09ca-e4d5-477a-a6c8-8c56b48f928e · outbound

This paper cites Visual7w: Grounded question answering in images.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Visual7w: Grounded question answering in images

Reference 34

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Observation 1be332be-6cf8-4e31-9040-ebfaa80a41a8 · outbound

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Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Unresolved cited work

Reference 36

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Observation 83a36de8-3632-41b7-93dc-e3e9ff4fc514 · outbound

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Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Unresolved cited work

Reference 37

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Observation dc3c4422-2004-4480-b7f5-4459846cb8a9 · outbound

This paper cites The table shows the number of fan letters for each day.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation The table shows the number of fan letters for each day

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.368364Z

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Observation c92ee23a-db34-49bb-b932-050b9e5fa0dd · outbound

This paper cites Opening Balance.\.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Opening Balance.\

Reference 39

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T20:24:57.352221Z

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Observation bb02a9a4-8ac3-4115-bb41-4aa687633838 · outbound

This paper cites Thus, the total number of fan letters received on Thursday and Monday is: 204 + 271 = 475.

Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation Thus, the total number of fan letters received on Thursday and Monday is: 204 + 271 = 475

Reference 204

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:24:57.413154Z

Source-reported events for the cited work

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Pith citing papers

Observation 71c9b1fc-2c69-43b6-8b4d-c87e0865845c · inbound

Balancing Image Compression and Generation with Bootstrapped Tokenization cites this paper.

Balancing Image Compression and Generation with Bootstrapped Tokenization Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:55.444845Z

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Observation e9852caa-6d97-453e-b997-8bb10ba5d27c · inbound

Answer-Conditioned Chain-of-Thought Distillation for Few-Shot Industrial Vision with Small VLMs cites this paper.

Answer-Conditioned Chain-of-Thought Distillation for Few-Shot Industrial Vision with Small VLMs Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T10:06:52.822171Z

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