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Paper Citation Record · LEDGER

Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.00658.

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

pith.paper-citation-record.v1
2202.00658 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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:04:57.577137Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:33:30.453084Z

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 e00296a3-5f2a-456b-9fc5-04d81aefba42 · inbound

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra cites this paper.

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T21:04:57.577137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:04:57.577137Z digest=sha256:b38ac0cf3ed01b739bf1152d9153d2b1d9106b6eb40492d05e36b0bba8d94b55

Observation 8230de2c-a4ee-4a1a-8767-a358beb00573 · inbound

AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning cites this paper.

AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:33:30.454894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-29T14:32:02.974833Z digest=sha256:6ab190035c8b5955ea21d31724ea31ba9d2f0c93082178858455640000593867