Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.15721.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:19:45.703566Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T16:58:12.119272Z
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 5e3f8163-2470-4627-877c-040c79062a2d · inbound
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions cites this paper.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Reference 143
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T02:46:27.643143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 503ae8ae-5719-42b0-b0d1-1def6e291805 · inbound
Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs cites this paper.
Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:19:45.703566Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6b1496e-14c7-4a4b-bead-7f09cf0dbff9 · inbound
VidHal: Benchmarking Temporal Hallucinations in Vision LLMs cites this paper.
VidHal: Benchmarking Temporal Hallucinations in Vision LLMs Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T16:58:12.123182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation bd7802e7-7331-4853-80c4-b4bd93232b61 · inbound
Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning cites this paper.
Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:20:03.680514Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 636caf5a-42f2-439e-8b1f-01e6ee878bdb · inbound
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance cites this paper.
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:46:02.896526Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.