Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.17453.
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 2026-08-07T04:58:46.495364Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T00:20:38.836850Z
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 a7fbfc17-c7ba-4ff7-bc93-5baf91af2076 · inbound
The Curious Language Model: Strategic Test-Time Information Acquisition cites this paper.
The Curious Language Model: Strategic Test-Time Information Acquisition Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:58:46.495364Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 429131e5-43ef-4e3c-8b1c-f1a6cabfee7c · inbound
Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution cites this paper.
Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T00:20:38.840104Z
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.