Paper Citation Record · LEDGER

A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2302.11097.

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

pith.paper-citation-record.v1
2302.11097 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:59:45.127296Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T00:41:56.051676Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation dc097915-bef1-4b3e-92cf-c4fd4a52cd82 · inbound

Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring cites this paper.

Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:45.127296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:59.721791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a251115-a9b8-427d-97e5-6229351f80b8 · inbound

Visual question answering: from early developments to recent advances -- a survey cites this paper.

Visual question answering: from early developments to recent advances -- a survey A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

Reference 233

Resolution: unresolved; no resolver link, observed 2026-08-10T21:46:29.176620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eb36545-2e5a-4d2e-bdd0-620a7d0e8675 · inbound

RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems? cites this paper.

RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems? A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T18:30:56.575437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 883bd9c9-8ef6-468d-bce5-508d5b03e528 · inbound

Towards Geometry Problem Solving in the Large Model Era: A Survey cites this paper.

Towards Geometry Problem Solving in the Large Model Era: A Survey A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:41.682159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cef20cfe-2727-4adc-b04f-e19bdd7459fb · inbound

GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines cites this paper.

GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary Lines A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-19T00:41:56.053944Z

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.