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Paper Citation Record · LEDGER

Towards Robust Toxic Content Classification

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.06872.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1912.06872 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:06:33.422169Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T01:47:31.679256Z

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 67b02e64-880c-4657-a461-a3725bef93ae · inbound

MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models cites this paper.

MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models Towards Robust Toxic Content Classification

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T15:06:33.422169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:06:33.422169Z digest=sha256:c7560afe05d3c2b7f848b593a739c065a1f91e75bb1139e0670bf6cc22ced37b

Observation fe1b1dcd-7f42-49f1-8ae9-66976e758abc · inbound

What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks cites this paper.

What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks Towards Robust Toxic Content Classification

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-07-03T01:47:31.680638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-27T16:18:01.850874Z digest=sha256:93f48b739bd146b090bbe9429ed61115d0f91738c66049f5bf1834ea68255d4f