Paper Citation Record · LEDGER

Can Large Language Models Understand Symbolic Graphics Programs?

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2408.08313.

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

pith.paper-citation-record.v1
2408.08313 v4

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 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:33:55.776114Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:48:35.116154Z

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 e5896bee-77f4-446b-a0f0-1376834f7baa · inbound

Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs cites this paper.

Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs Can Large Language Models Understand Symbolic Graphics Programs?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T23:33:55.776114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3da0100f-2fca-47a0-9b8e-424868692dbc · inbound

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency cites this paper.

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency Can Large Language Models Understand Symbolic Graphics Programs?

Reference 102

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:58:59.239889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e04dd7be-5a97-47a0-89e2-8817289af4fd · inbound

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning cites this paper.

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning Can Large Language Models Understand Symbolic Graphics Programs?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T20:55:10.004501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40711a87-a2a8-4f7b-b9c1-faefd8536faa · inbound

CodePercept: Code-Grounded Visual STEM Perception for MLLMs cites this paper.

CodePercept: Code-Grounded Visual STEM Perception for MLLMs Can Large Language Models Understand Symbolic Graphics Programs?

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T23:22:13.847876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72f681d8-204c-45a0-b9aa-4e86fa404f4b · inbound

mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval cites this paper.

mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval Can Large Language Models Understand Symbolic Graphics Programs?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:51:46.501995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58b1db78-6914-4ab2-bc87-f59313bc2305 · inbound

DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing cites this paper.

DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing Can Large Language Models Understand Symbolic Graphics Programs?

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:48:35.117528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.