Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2410.02642.
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 2026-08-05T17:15:15.252429Z
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
0
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 05841dfe-128f-40fb-8cb4-7837ea77ed7c · inbound
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models cites this paper.
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:15:15.252429Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6afa6d36-0767-4ad0-8225-1325231d5a08 · inbound
Where Relevance Emerges: A Layer-Wise Study of Internal Attention for Zero-Shot Re-Ranking cites this paper.
Where Relevance Emerges: A Layer-Wise Study of Internal Attention for Zero-Shot Re-Ranking Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T19:26:30.735984Z
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 91744e54-eac7-42f6-9732-e298f256bb8f · inbound
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning cites this paper.
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T13:59:01.287449Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc7f3c78-31f3-47ad-8c90-c4d364ef9cc3 · inbound
Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking cites this paper.
Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T08:27:51.810469Z
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 4ccb0ec8-0711-4c56-9aa2-1a5d8994a48b · inbound
Test-Time Training for Zero-Resource Dense Retrieval Reranking cites this paper.
Test-Time Training for Zero-Resource Dense Retrieval Reranking Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T21:36:14.715531Z
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 978be233-b0ea-4ade-9613-a484fadb70de · inbound
Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering cites this paper.
Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T17:13:45.312244Z
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 1d1aefd7-5da3-4aa1-b990-9e111e8f7143 · inbound
Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering cites this paper.
Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T13:49:15.991204Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.