Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.11541.
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 2026-08-07T13:54:27.484570Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:25:53.925320Z
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 8daa2f8f-666c-4e28-8e36-3d02f5d3ad70 · inbound
IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios cites this paper.
IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:54:27.484570Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d4b0177-89df-4dcd-af1b-fbf524377953 · inbound
A Collaborative Framework Integrating Large Language Model and Chemical Fragment Space: Mutual Inspiration for Lead Design cites this paper.
A Collaborative Framework Integrating Large Language Model and Chemical Fragment Space: Mutual Inspiration for Lead Design Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:25:53.931624Z
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 6e22a4b3-ef28-4308-ac66-9c78e95da126 · inbound
AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation cites this paper.
AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:51:01.917592Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.