Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.18365.
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 2026-08-07T10:54:38.196663Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T19:08:53.912216Z
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 d2089229-b190-428e-a234-7b3d34aeaba7 · inbound
AI Agents for Conversational Patient Triage: Preliminary Simulation-Based Evaluation with Real-World EHR Data cites this paper.
AI Agents for Conversational Patient Triage: Preliminary Simulation-Based Evaluation with Real-World EHR Data BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:54:38.196663Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf47fdff-9059-4797-aba7-7360d97e7e59 · inbound
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders cites this paper.
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:08:54.009071Z
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 07765743-dc68-461a-afd3-823c9bd0b019 · inbound
Large Language Models Meet Legal Artificial Intelligence: A Survey cites this paper.
Large Language Models Meet Legal Artificial Intelligence: A Survey BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T18:26:14.206564Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.