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Paper Citation Record · LEDGER

Defending against Insertion-based Textual Backdoor Attacks via Attribution

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.02394.

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

pith.paper-citation-record.v1
2305.02394 v2

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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:17:07.314042Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T23:28:40.675274Z

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 f9ab14bf-c74d-4e11-b9f9-c94e9643bec2 · inbound

Concept-Level Explainability for Auditing & Steering LLM Responses cites this paper.

Concept-Level Explainability for Auditing & Steering LLM Responses Defending against Insertion-based Textual Backdoor Attacks via Attribution

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T22:17:07.314042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:17:07.314042Z digest=sha256:4a6a33c496eac9a4a4109cd3bcb472c5a421dd58ea31f31dcd25d79c786cbb11

Observation 9de428be-f9f2-48ef-a5b7-06bd4dc63870 · inbound

SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models cites this paper.

SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models Defending against Insertion-based Textual Backdoor Attacks via Attribution

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:28:40.677618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-16T23:26:48.405593Z digest=sha256:9728b31625dbdbd18c9b04ad8c2760008bf5c49c4b7d77ea0e03eb5104493758