Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.10690.
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 2026-08-07T11:10:45.622334Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T11:07:15.373788Z
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 41c39a13-2c28-46cd-89d4-ccd9cc75da88 · inbound
Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows cites this paper.
Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:10:45.622334Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6b3d5b6-82f7-429e-bf0e-153f45c337c7 · inbound
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs cites this paper.
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T11:07:15.375200Z
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.
Observation 05bd72e4-c2f2-451e-a855-61ccfd0638d4 · inbound
To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs cites this paper.
To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T00:05:50.861349Z
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.