Paper Citation Record · LEDGER

$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2404.14986.

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

pith.paper-citation-record.v1
2404.14986 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:46:02.945809Z

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

0
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 8a1e7de7-0583-47a4-b012-c1fa3c7da80b · inbound

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification? cites this paper.

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification? $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:59.537695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d203253-1816-4ca3-b84e-67798d902b45 · inbound

Molecular Machine Learning in Chemical Process Design cites this paper.

Molecular Machine Learning in Chemical Process Design $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T16:46:02.945809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee31ce93-068e-43c1-bb15-68318e957b69 · inbound

CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation cites this paper.

CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:46:08.338587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8664ad5-4876-4890-9289-18f9e6c58cc1 · inbound

Tabular foundation models for in-context prediction of molecular properties cites this paper.

Tabular foundation models for in-context prediction of molecular properties $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.437089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 251e48bc-c6a3-41f1-9ddd-8f3a679fe2da · inbound

Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale cites this paper.

Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:25:56.466528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 070a1f8d-7b12-4c81-a0cb-efc1fa15301f · inbound

Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale cites this paper.

Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:29:09.204356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 51382735-f6e4-455f-8bf3-0cae5d93ed01 · inbound

Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale cites this paper.

Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:05:07.367275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f43a112a-be3d-49a9-9d90-558a871e7b87 · inbound

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction cites this paper.

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-27T14:00:59.353183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.