Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.14449.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T20:42:38.877409Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T04:37:31.877056Z
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 54876099-9c2f-434c-934b-060a1d2d17e3 · inbound
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models cites this paper.
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:37:31.880471Z
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 fd4e926d-5de4-415c-afbc-953b5cd03ac8 · inbound
Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency cites this paper.
Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:42:38.877409Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 490dfacd-9344-49fc-b389-893570f73156 · inbound
RankLLM: A Python Package for Reranking with LLMs cites this paper.
RankLLM: A Python Package for Reranking with LLMs APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:20:39.140380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60187040-796e-46b2-b31e-a1d92165ba5a · inbound
Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs cites this paper.
Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:34:14.544195Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.