Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1705.10843.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T00:15:55.904250Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T08:19:44.808305Z
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 87c211ba-9148-44a8-ba02-a5d3c4b27096 · inbound
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design cites this paper.
Quantum-inspired Reinforcement Learning for Synthesizable Drug Design Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Reference 22
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-23T20:33:25.812640Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 47fdcaeb-9722-4469-9d5f-94088322dbb3 · inbound
Quantum latent distributions in deep generative models cites this paper.
Quantum latent distributions in deep generative models Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:33:13.259861Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1b0f905-ed1e-4195-af19-07185964eaa2 · inbound
How Creative Are Large Language Models in Generating Molecules? cites this paper.
How Creative Are Large Language Models in Generating Molecules? Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Reference 34
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T12:01:00.577075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7db8dde3-2d38-46dd-a3a0-c8ea60e65f90 · inbound
MolWorld: Molecule World Models for Actionable Molecular Optimization cites this paper.
MolWorld: Molecule World Models for Actionable Molecular Optimization Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T01:51:14.354638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 37fb63bd-43dc-411b-b333-694fbb8d9d47 · inbound
BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language cites this paper.
BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T08:19:44.809668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f5f4fcc1-d558-4792-a116-f3f132cdc1af · inbound
Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation cites this paper.
Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T13:39:08.539015Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cede7108-d4bb-4b55-ad61-a51ddebd4509 · inbound
The Curious Case of the Default Settings: Evaluating Default Performance of Variational Inference Software cites this paper.
The Curious Case of the Default Settings: Evaluating Default Performance of Variational Inference Software Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T00:15:55.904250Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.