Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.18064.
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 2026-08-07T12:09:55.988580Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T07:22:09.162906Z
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 53070168-532d-41d6-9a2d-7049534bf9d1 · inbound
How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG cites this paper.
How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:09:55.988580Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3b8bc7e8-0d03-450b-8ab7-c404c8a69afa · inbound
MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering cites this paper.
MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T07:22:09.165702Z
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 f486ed0f-a585-4b3b-9201-fb4ac15364dd · inbound
HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling cites this paper.
HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T13:34:30.939742Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.