Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Survey on the Honesty of Large Language Models
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2409.18786.
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 2026-08-06T14:13:05.968679Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T22:46:53.316542Z
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 e899c8c1-bf0e-4e9d-ad75-f000d663a248 · inbound
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges A Survey on the Honesty of Large Language Models
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:13:05.968679Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5120098-b358-43ea-a260-d017bec73ec7 · inbound
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends cites this paper.
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends A Survey on the Honesty of Large Language Models
Reference 88
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T22:46:53.319545Z
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 fe01afdb-4e61-44e8-be2c-c0db31fc805a · inbound
The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination cites this paper.
The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination A Survey on the Honesty of Large Language Models
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T04:10:51.324392Z
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 25871147-2f7e-464f-b55a-1cc059a7788b · inbound
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models cites this paper.
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models A Survey on the Honesty of Large Language Models
Reference 175
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T12:40:54.740662Z
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 bc35caee-09be-4c7a-92a5-89e2d0a2816c · inbound
Emergent Social Intelligence Risks in Generative Multi-Agent Systems cites this paper.
Emergent Social Intelligence Risks in Generative Multi-Agent Systems A Survey on the Honesty of Large Language Models
Reference 76
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T21:48:00.883086Z
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 6890b531-e64a-4738-b125-710470d33104 · inbound
From Scalars to Tensors: Declared Losses Recover Epistemic Distinctions That Neutrosophic Scalars Cannot Express cites this paper.
From Scalars to Tensors: Declared Losses Recover Epistemic Distinctions That Neutrosophic Scalars Cannot Express A Survey on the Honesty of Large Language Models
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T14:30:03.661300Z
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 3ff39fd8-0566-47c2-92e0-929981530ab8 · inbound
MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models cites this paper.
MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models A Survey on the Honesty of Large Language Models
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T01:23:33.837195Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.