Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2312.05434.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T15:37:54.799232Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T13:54:43.724006Z
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 c7f08861-d96a-419a-ae7f-62b97d67b7d1 · inbound
Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities cites this paper.
Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
Reference 81
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:48:39.091290Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a50f6d16-a19a-4952-a8ad-922d17c470f1 · inbound
Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes cites this paper.
Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T13:54:43.725459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 09f79d5a-abcd-4ac5-aa13-170674144430 · inbound
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges cites this paper.
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:37:54.799232Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.