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Paper Citation Record · LEDGER

Mitigating Trojanized Prompt Chains in Educational LLM Use Cases: Experimental Findings and Detection Tool Design

As of 13 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 0 inbound Pith citation observations for arXiv:2507.14207.

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

pith.paper-citation-record.v1
2507.14207 v1

Coverage vector

measured 2 of 2 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:23:27.757486Z

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

2 of 2 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5fbfe846-66af-4bc8-961e-5f4def5cc5f3 · outbound

This paper cites Training Compute of Frontier AI Models Grows by 4-5x per Year.

Mitigating Trojanized Prompt Chains in Educational LLM Use Cases: Experimental Findings and Detection Tool Design Training Compute of Frontier AI Models Grows by 4-5x per Year

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:23:27.876005Z

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.

source=pdf_text observed=2026-08-06T17:23:27.707901Z digest=sha256:66774c94b5182b1b1c308e601ca861fc89f777f656f1357ddbc42841d61bfbea

Observation 2306a272-fc10-491b-98a4-90e2bf3eb8cd · outbound

This paper cites Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science.

Mitigating Trojanized Prompt Chains in Educational LLM Use Cases: Experimental Findings and Detection Tool Design Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T17:23:27.757486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:23:27.757486Z digest=sha256:2b9bef5bafc4b2a228ada0a0d968083f23ad648484581215e5192c4fb18415ee

Pith citing papers

No inbound Pith citation observations are available.