Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.14454.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:27:44.516169Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T18:42:29.071864Z
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 96a8f860-89cf-450a-8125-f2f81fccd38f · inbound
AutoData: A Multi-Agent System for Open Web Data Collection cites this paper.
AutoData: A Multi-Agent System for Open Web Data Collection Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:27:44.516169Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 073b46af-19c6-4efd-b97c-a8f5e8cf3719 · inbound
Fine-tuning on simulated data outperforms prompting for agent tone of voice cites this paper.
Fine-tuning on simulated data outperforms prompting for agent tone of voice Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:42:01.551848Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8179a10f-1b05-4d8d-a1ce-be14d28f1773 · inbound
CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification cites this paper.
CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:01:23.434340Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdfeb420-9850-486b-85f9-2856966438ab · inbound
Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets cites this paper.
Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:27:42.136860Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9094fa3-5973-44ba-8609-e1761bc10aa5 · inbound
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges cites this paper.
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Reference 109
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T18:42:29.073311Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.