Paper Citation Record · LEDGER

MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2406.12950.

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

pith.paper-citation-record.v1
2406.12950 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:19:27.836650Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

12
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 233b0267-7111-4453-985a-8fdff864e4d2 · inbound

Retrieval-Augmented Generation with Graphs (GraphRAG) cites this paper.

Retrieval-Augmented Generation with Graphs (GraphRAG) MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 265

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T04:33:39.726939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfaba502-e9f3-45ad-9189-e5e0bbbe85a3 · inbound

Computational Protein Science in the Era of Large Language Models (LLMs) cites this paper.

Computational Protein Science in the Era of Large Language Models (LLMs) MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-10T19:19:27.836650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e04eeff8-7628-44a2-8eb9-7805a14bdd1c · inbound

GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design cites this paper.

GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T14:01:38.391047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c7fa607-fb1b-4025-aa83-f34b4fb45737 · inbound

MLaGA: Multimodal Large Language and Graph Assistant cites this paper.

MLaGA: Multimodal Large Language and Graph Assistant MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:26:42.025148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfb5522a-3c62-47b4-8832-92cf9181fb89 · inbound

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks cites this paper.

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:59.977288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4840c676-ad96-4253-9269-052f00f9bd06 · inbound

SLASH the Sink: Sharpening Structural Attention Inside LLMs cites this paper.

SLASH the Sink: Sharpening Structural Attention Inside LLMs MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:56:25.026189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c771095a-ae54-4f2b-8eeb-efd6f1c068eb · inbound

SLASH the Sink: Sharpening Structural Attention Inside LLMs cites this paper.

SLASH the Sink: Sharpening Structural Attention Inside LLMs MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T03:32:12.344613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d26e4510-56d5-4045-aec9-7e41de22fe7a · inbound

SLASH the Sink: Sharpening Structural Attention Inside LLMs cites this paper.

SLASH the Sink: Sharpening Structural Attention Inside LLMs MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:23:48.045367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a519e517-9c4a-4d4d-9d3f-fcd17ddf5074 · inbound

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

Reference 96

Resolution: verified exact; arxiv_id, observed 2026-06-27T17:31:07.347013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9107baac-ba6b-4739-9464-7c26bda5461b · inbound

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-02T12:03:33.399831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.