Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2405.09939.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:35:57.983501Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T19:52:35.957340Z
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 8debc842-a257-4994-921d-011914977b95 · inbound
Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control cites this paper.
Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:35:57.983501Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 385aec29-5438-4db1-b61d-04173001aedb · inbound
MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval cites this paper.
MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:57:04.516424Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6e12bf77-e684-457e-bef2-6d6a9ee9706c · inbound
Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models cites this paper.
Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T07:42:09.120088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 31840d20-f8dd-4453-be42-9e1ef8eb0b8e · inbound
Towards a Large Physics Benchmark cites this paper.
Towards a Large Physics Benchmark SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:34:57.594431Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 55d14a66-e979-4171-89b7-f9e93e402475 · inbound
ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge cites this paper.
ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Reference 19
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T03:32:01.565921Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 468a4674-b87b-4830-a98f-0092981ab8a1 · inbound
Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery cites this paper.
Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:57:47.951224Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc5440d3-b1f8-4804-9d71-cb06a1ce6f2a · inbound
ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment cites this paper.
ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-28T19:52:35.959493Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.