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Paper Citation Record · LEDGER

SemAttack: Natural Textual Attacks via Different Semantic Spaces

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2205.01287.

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

pith.paper-citation-record.v1
2205.01287 v3

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:21.948449Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:00:09.057633Z

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 730e4317-d668-4a8f-9c17-8ed16f214329 · inbound

Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings cites this paper.

Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings SemAttack: Natural Textual Attacks via Different Semantic Spaces

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:21.948449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:21.948449Z digest=sha256:8f2084a61ddb96905e7b97a35a6700886380faa7dec13fb921b534e26b6adfdb

Observation cebed54e-308f-49c0-a0a0-c2881f677261 · inbound

Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning cites this paper.

Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning SemAttack: Natural Textual Attacks via Different Semantic Spaces

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-07-04T21:00:09.059395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-25T19:19:50.420396Z digest=sha256:76fc5111e1ea58f03336005a9564882f51656759c7c2ef3ceec90af984fad801