Paper Citation Record · LEDGER

Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2410.02642.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.02642 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:07:23.868466Z

measured 1 of 1 external citation measurements

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 822371d1-c826-44b3-b1a0-914380ef0264 · inbound

Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation cites this paper.

Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T12:07:23.868466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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.