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Paper Citation Record · LEDGER

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning

As of 12 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.10455.

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

pith.paper-citation-record.v1
2412.10455 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:25:59.727153Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved23
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External citation measurements

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Outbound references

Observation 42c1d456-5328-42e2-8305-5f612429add0 · outbound

This paper cites an unresolved cited work.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Unresolved cited work

Reference 1

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 3482d603-b30a-41b2-81f2-77a7072abfe0 · outbound

This paper cites GPT-4 Technical Report.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning GPT-4 Technical Report

Reference 2

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Observation 19d30cb8-7b42-4911-9825-16b29ee047ff · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Flamingo: a visual language model for few-shot learning

Reference 3

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Observation 7739cef1-6b0c-4eee-8f91-36a4dc1c880c · outbound

This paper cites PaLM 2 Technical Report.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning PaLM 2 Technical Report

Reference 4

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Observation 09fac0a4-273d-43a6-9c8c-94bd919d1303 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 5

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Observation 5d9fc6c4-4b6b-4707-89fd-3109079c1601 · outbound

This paper cites Llemma: An Open Language Model For Mathematics.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Llemma: An Open Language Model For Mathematics

Reference 6

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Observation 7aec78bd-8283-46d5-bcf2-d4935b7038da · outbound

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

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 7

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Observation a6ab68e3-529a-4013-b27d-d2f5b5935512 · outbound

This paper cites An Introduction to Vision-Language Modeling.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning An Introduction to Vision-Language Modeling

Reference 8

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Observation e44c080d-06b0-441e-8b44-3d74dd16952f · outbound

This paper cites An augmented benchmark dataset for geometric ques- tion answering through dual parallel text encoding.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning An augmented benchmark dataset for geometric ques- tion answering through dual parallel text encoding

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 48bb2c5f-ad73-4e7a-9f66-84a4cc5edecf · outbound

This paper cites UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression

Reference 10

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Observation 062308c0-b4b0-44de-9902-75f3c5f169df · outbound

This paper cites GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning

Reference 11

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Observation 626f9503-631e-491b-b84b-71f442e6b574 · outbound

This paper cites an unresolved cited work.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Unresolved cited work

Reference 12

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Observation d1bf7249-70b2-44e3-a385-fd9e49d366e3 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 13

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Observation 93a4b07e-eff0-4d14-b528-dcee73380ec4 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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Observation 71597bc7-83f3-4e3b-b79b-bcf4ed79b709 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Model-agnostic meta-learning for fast adaptation of deep networks

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:25:59.595287Z digest=sha256:419e9da2094c89020e92e5b86a4f0bec757feb1f7c6233bb3d82eabdda8f68f9

Observation ba1ec2b5-5112-4d1e-be3d-dd4c019c1646 · outbound

This paper cites G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model

Reference 16

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Observation 63bffd6c-7f16-40b2-b0c2-dd93a8550c2b · outbound

This paper cites Google bard.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Google bard

Reference 17

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Observation 63ab0e64-c6e7-4573-ae02-375b2e7eaf33 · outbound

This paper cites M., and Le, Q.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning M., and Le, Q

Reference 18

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 05383a08-5fd9-4d2f-9762-ef84482bbe00 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 19

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Observation 9421807c-1712-43c4-9f58-5657535afa2d · outbound

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

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large language models

Reference 20

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 5a2428bd-b884-4392-9391-2efe91ecfd0f · outbound

This paper cites Unimath: A foundational and multimodal mathematical reasoner.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Unimath: A foundational and multimodal mathematical reasoner

Reference 21

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c3b473fc-d421-4d78-9a2e-9c71c810de87 · outbound

This paper cites an unresolved cited work.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Unresolved cited work

Reference 22

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c41773ed-e660-4b5c-9e14-bacc015b7e68 · outbound

This paper cites UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning

Reference 23

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Observation 66882d7b-e4d7-4d05-a27a-4370397ed705 · outbound

This paper cites MetaICL: Learning to Learn In Context.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning MetaICL: Learning to Learn In Context

Reference 24

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Observation 19112242-efe9-4285-bb88-68f3950b8631 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 25

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Observation ae809290-80d8-4e89-9bb8-514e3c79a6b7 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I

Reference 26

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 1576e6d7-4808-42c4-889d-19758b05e314 · outbound

This paper cites Optimization as a model for few-shot learning.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Optimization as a model for few-shot learning

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T17:25:59.676577Z digest=sha256:3a2f0adf8c27ac72b294fb9404b82fd2e8341beeca324bf59bba3b7bd15163e0

Observation 9684f460-3b51-452e-b028-a98a259d8cc5 · outbound

This paper cites Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-

Reference 28

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raw_fallback, observed 2026-08-11T17:26:00.255580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation f7aaf68d-1907-4119-8093-fe3eef7f296b · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

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source=pdf_text observed=2026-08-11T17:25:59.691022Z digest=sha256:612210c7d98e66cdcbc3efaa2acb8efa7c7fb1ccf1063a845e1a89c79a697d66

Observation 1bc44334-7b2d-4a39-a9e2-1afb6e5310c5 · outbound

This paper cites H., Wu, Y., Le, Q.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning H., Wu, Y., Le, Q

Reference 30

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raw_fallback, observed 2026-08-11T17:26:00.235539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 1af341c0-c2ab-4aa4-a961-996ca1c0ecb4 · outbound

This paper cites MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning

Reference 31

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source=pdf_text observed=2026-08-11T17:25:59.702693Z digest=sha256:d6daa072e4ca5234f76429979e70366feecada8831909e150e5bdb6973822dda

Observation 175b283a-c117-4639-b775-6f677e43362c · outbound

This paper cites Large Language Models are Better Reasoners with Self-Verification.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning Large Language Models are Better Reasoners with Self-Verification

Reference 32

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source=pdf_text observed=2026-08-11T17:25:59.713082Z digest=sha256:af7f9dd3e1a5c05b41a59439b8e777be794e1be8f7e9f04beafe7d2045e97007

Observation f8e607f3-25e3-436d-bd0d-70964b307721 · outbound

This paper cites A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

Reference 33

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source=pdf_text observed=2026-08-11T17:25:59.721791Z digest=sha256:b5ffdd6abc59bf3b3a82a8c5c4c7f44169c1510e18e17afcbd8740f18980abcc

Observation f4e86470-a670-43c5-a38c-7019f292dd17 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:25:59.727153Z digest=sha256:988d4201e64ada252250d5e779580ac4ee330c170f51d0bccfbaa070b2fdabc5

Pith citing papers

No inbound Pith citation observations are available.