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Paper Citation Record · LEDGER

Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2504.02606.

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

pith.paper-citation-record.v1
2504.02606 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:46:02.989416Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T15:39:26.573512Z

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 bd078441-f737-43c7-b2ac-8c8447fb53f0 · inbound

Can Hallucinations Help? Boosting LLMs for Drug Discovery cites this paper.

Can Hallucinations Help? Boosting LLMs for Drug Discovery Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-10T15:39:26.578593Z

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.

source=arxiv_source observed=2026-08-10T15:39:26.431779Z digest=sha256:22b9ae58c29f0aeb23ae1e17f02f0cbc71d46973f476a622376c08305a202fc5

Observation 9e1eb9dc-c414-4731-bcb4-bd7ac44e0ee8 · inbound

Molecular Machine Learning in Chemical Process Design cites this paper.

Molecular Machine Learning in Chemical Process Design Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification

Reference 103

Resolution
unresolved
no resolver link, observed 2026-08-15T16:46:02.989416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:46:02.989416Z digest=sha256:90a167ffb6169330309c07f87de0d844addc83872ce49b74f591bb75165863cd