Paper Citation Record · LEDGER

A Survey of Deep Learning for Geometry Problem Solving

As of 12 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2507.11936.

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

pith.paper-citation-record.v1
2507.11936 v6

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:01:33.977594Z

measured 30 of 30 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:05:45.046156Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T06:09:41.487349Z

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy11
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5b77efc-6c5b-4102-abec-cd78b5735100 · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

A Survey of Deep Learning for Geometry Problem Solving ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:32.025576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b05909c3-ed50-4e79-a09e-48d6f282daf5 · outbound

This paper cites The Llama 3 Herd of Models.

A Survey of Deep Learning for Geometry Problem Solving The Llama 3 Herd of Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:32.103437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 812e7232-9d85-48ea-8979-beedae8c87ff · outbound

This paper cites InProceedings of the 21st International Conference on Intelligent User Interfaces, pages 419–430.

A Survey of Deep Learning for Geometry Problem Solving InProceedings of the 21st International Conference on Intelligent User Interfaces, pages 419–430

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:36.709823Z

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.

Observation 9f1d2ad3-2867-4d60-bb69-95a5d0c13c49 · outbound

This paper cites MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program Fine-tuning.

A Survey of Deep Learning for Geometry Problem Solving MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program Fine-tuning

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:01:34.672118Z

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.

Observation f6c71c35-17bd-4a3e-9b64-60c1c9e50d80 · outbound

This paper cites InProceedings of the 15th International Conference on Education Technology and Computers, pages 308– 314.

A Survey of Deep Learning for Geometry Problem Solving InProceedings of the 15th International Conference on Education Technology and Computers, pages 308– 314

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:36.407615Z

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.

Observation 8333d0fc-1adf-4f41-aa6d-01b180f89777 · outbound

This paper cites MC-NEST: Enhancing Mathematical Reasoning in Large Language Models leveraging a Monte Carlo Self-Refine Tree.

A Survey of Deep Learning for Geometry Problem Solving MC-NEST: Enhancing Mathematical Reasoning in Large Language Models leveraging a Monte Carlo Self-Refine Tree

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:32.769139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42a47730-0546-46c1-8071-3a7fb5afb8ff · outbound

This paper cites Proximal Policy Optimization Algorithms.

A Survey of Deep Learning for Geometry Problem Solving Proximal Policy Optimization Algorithms

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:32.825059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12d1481f-a422-4f0b-81e5-e89bbe938a1f · outbound

This paper cites Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning.

A Survey of Deep Learning for Geometry Problem Solving Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:01:34.459156Z

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.

Observation a2da9144-3439-4228-94f4-2d69ab891af3 · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

A Survey of Deep Learning for Geometry Problem Solving Yi: Open Foundation Models by 01.AI

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:33.070929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75bcfe0a-144f-4192-b278-2b55000481af · outbound

This paper cites Chengke Zou, Xingang Guo, Rui Yang, Junyu Zhang, Bin Hu, and Huan Zhang.

A Survey of Deep Learning for Geometry Problem Solving Chengke Zou, Xingang Guo, Rui Yang, Junyu Zhang, Bin Hu, and Huan Zhang

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:36.065292Z

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.

Observation 7a189bd5-445c-4a04-b286-781329dcfc79 · outbound

This paper cites an unresolved cited work.

A Survey of Deep Learning for Geometry Problem Solving Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:01:35.727578Z

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.

Observation 7983dd51-615b-481f-94ca-7456b67514c6 · outbound

This paper cites C Encoder-Decoder Architecture for Geometry Problem Solving In this section, we further elaborate on the deep learning components of the encoder-decoder archi- tecture used for GPS.

A Survey of Deep Learning for Geometry Problem Solving C Encoder-Decoder Architecture for Geometry Problem Solving In this section, we further elaborate on the deep learning components of the encoder-decoder archi- tecture used for GPS

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:35.574816Z

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.

Observation 6a910bb7-e252-4602-88f2-d2fee80881f0 · outbound

This paper cites an unresolved cited work.

A Survey of Deep Learning for Geometry Problem Solving Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:01:35.392422Z

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.

Observation 1426398f-9734-43eb-aa61-10133cf64cd3 · outbound

This paper cites Other studies employ pre-trained language models as decoders.

A Survey of Deep Learning for Geometry Problem Solving Other studies employ pre-trained language models as decoders

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:35.237435Z

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.

Observation 36ecac96-7289-4e77-a6ea-e84cffd2f3bb · outbound

This paper cites In addi- tion, Zhang et al.

A Survey of Deep Learning for Geometry Problem Solving In addi- tion, Zhang et al

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:35.104727Z

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.

Observation 6af0e664-4406-41af-9aff-4bc88a65a64c · outbound

This paper cites Answer Verifier.Ensuring the correctness of the solution logic is one of the key steps in solv- ing geometry problems.

A Survey of Deep Learning for Geometry Problem Solving Answer Verifier.Ensuring the correctness of the solution logic is one of the key steps in solv- ing geometry problems

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:34.969526Z

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.

Observation ecf702d6-f9ca-4584-bc4e-822ba3c6076a · outbound

This paper cites MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models.

A Survey of Deep Learning for Geometry Problem Solving MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models

Reference 859

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:32.689405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3a7565d-e0fc-48a8-aa61-ebaffae6d521 · outbound

This paper cites A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?.

A Survey of Deep Learning for Geometry Problem Solving A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?

Reference 1643

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:33.359052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27527bad-35e6-4963-8e65-772d41cb5d41 · outbound

This paper cites 1https://www.geogebra.org This task is also related to GPS.

A Survey of Deep Learning for Geometry Problem Solving 1https://www.geogebra.org This task is also related to GPS

Reference 2004

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:35.904886Z

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.

Observation 30dad8bf-bf59-4693-a14a-e2b76dcf6a4b · outbound

This paper cites In2015 International Confer- ence of Educational Innovation through Technology (EITT), pages 46–50.

A Survey of Deep Learning for Geometry Problem Solving In2015 International Confer- ence of Educational Innovation through Technology (EITT), pages 46–50

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:36.253229Z

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.

Observation c5b005a1-506d-445f-af5c-4225d1210a06 · outbound

This paper cites Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning.

A Survey of Deep Learning for Geometry Problem Solving Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:31.788582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b4f257c-0f60-4143-8fa6-93dc46bedb87 · outbound

This paper cites PRM-BAS: Enhancing Multimodal Reasoning through PRM-guided Beam Annealing Search.

A Survey of Deep Learning for Geometry Problem Solving PRM-BAS: Enhancing Multimodal Reasoning through PRM-guided Beam Annealing Search

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:32.183834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1fa5ac2-5ee4-4f1d-af57-97d4227e128b · outbound

This paper cites Maizhen Ning, Qiu-Feng Wang, Kaizhu Huang, and Xiaowei Huang.

A Survey of Deep Learning for Geometry Problem Solving Maizhen Ning, Qiu-Feng Wang, Kaizhu Huang, and Xiaowei Huang

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:36.556663Z

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.

Observation da373034-26dd-401d-b93d-14e5b682b48f · outbound

This paper cites HARP: A challenging human-annotated math reasoning benchmark.

A Survey of Deep Learning for Geometry Problem Solving HARP: A challenging human-annotated math reasoning benchmark

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:33.201477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f19d72e4-e2a1-47be-a3bc-545f510277a7 · outbound

This paper cites InInternational Conference on Learning Representations.

A Survey of Deep Learning for Geometry Problem Solving InInternational Conference on Learning Representations

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:01:36.868387Z

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.

Observation f4f4b6b1-e2fe-412f-8028-be0a77cc6c71 · outbound

This paper cites Hologram Reasoning for Solving Algebra Problems with Geometry Diagrams.

A Survey of Deep Learning for Geometry Problem Solving Hologram Reasoning for Solving Algebra Problems with Geometry Diagrams

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T17:01:34.797676Z

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.

Observation b8e31a54-55f4-4ebd-8316-ccfb0d05a375 · outbound

This paper cites Virgo: A Preliminary Exploration on Reproducing o1-like MLLM.

A Survey of Deep Learning for Geometry Problem Solving Virgo: A Preliminary Exploration on Reproducing o1-like MLLM

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:31.946017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 428ffeca-aa12-4c59-9a17-0eaa3d16a726 · outbound

This paper cites Tengjin Weng, Jingyi Wang, Wenhao Jiang, and Zhong Ming.

A Survey of Deep Learning for Geometry Problem Solving Tengjin Weng, Jingyi Wang, Wenhao Jiang, and Zhong Ming

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:32.987807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d833c301-794e-46cc-844e-e2cc56a0eb09 · outbound

This paper cites GAPS: Geometry-Aware Problem Solver.

A Survey of Deep Learning for Geometry Problem Solving GAPS: Geometry-Aware Problem Solver

Reference 2026

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:01:34.148764Z

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.

Pith citing papers

Observation b42bc3a7-adde-4292-8275-cfd699e4fa51 · inbound

Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interaction cites this paper.

Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interaction A Survey of Deep Learning for Geometry Problem Solving

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-12T02:09:07.377515Z

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.