Paper Citation Record · LEDGER

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.04292.

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

pith.paper-citation-record.v1
2310.04292 v3

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 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:46:02.941891Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:56:09.148009Z

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 3e689c0f-b6c7-4a13-b8f5-a3f4be857df2 · inbound

Chemist-aligned retrosynthesis by ensembling diverse inductive bias models cites this paper.

Chemist-aligned retrosynthesis by ensembling diverse inductive bias models Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T20:56:33.327463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da527c5d-ec37-45e9-ae77-8a6cbd17f570 · inbound

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery cites this paper.

M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T20:23:44.440078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07bc5733-ad40-462d-9ac9-813099f21a48 · inbound

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction cites this paper.

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 1978

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:13.575638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89f7cfed-bebf-4386-bd29-38557aa43a6a · inbound

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection cites this paper.

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T22:13:54.957511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a018b8e-6728-43a6-a695-754b15758675 · inbound

Self-Refining Training for Amortized Density Functional Theory cites this paper.

Self-Refining Training for Amortized Density Functional Theory Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T11:56:09.150837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8509804c-7c1d-49bf-a663-142a5e15fabc · inbound

Molecular Machine Learning in Chemical Process Design cites this paper.

Molecular Machine Learning in Chemical Process Design Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T16:46:02.941891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.