Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2106.05346.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:08:11.818789Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
8
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation a8d65457-05b2-4453-8e4f-55135ac4c321 · inbound
Improving language models by retrieving from trillions of tokens cites this paper.
Improving language models by retrieving from trillions of tokens End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Reference 114
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T12:55:41.666201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 79cee84a-9c5c-4705-ab9f-26cc7183f204 · inbound
Atlas: Few-shot Learning with Retrieval Augmented Language Models cites this paper.
Atlas: Few-shot Learning with Retrieval Augmented Language Models End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T13:48:43.177563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0dd5f360-aad5-4470-bc83-4aa4d7617ab1 · inbound
Atlas: Few-shot Learning with Retrieval Augmented Language Models cites this paper.
Atlas: Few-shot Learning with Retrieval Augmented Language Models End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Reference 241
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T13:48:43.473455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e149823a-f62d-4094-b560-baea7c623070 · inbound
A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions cites this paper.
A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Reference 76
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:08:11.818789Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.