Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.15828.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:45:08.575958Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T22:17:26.426748Z
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 4807a835-a9d7-4da2-b879-7000fea00fb4 · inbound
AI4Research: A Survey of Artificial Intelligence for Scientific Research cites this paper.
AI4Research: A Survey of Artificial Intelligence for Scientific Research LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:45:08.575958Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ca1862a-7b04-4803-8b1e-de07ba5f414c · inbound
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution cites this paper.
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T00:15:09.128322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3060ff38-9817-4d0f-8a60-fb58e26cb2e0 · inbound
DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations cites this paper.
DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T22:17:26.428259Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 49af6565-fa1c-4c89-bac2-a41101a24e54 · inbound
DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations cites this paper.
DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:08:59.193661Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.