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Paper Citation Record · LEDGER

LLMDFA: Analyzing Dataflow in Code with Large Language Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.10754.

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

pith.paper-citation-record.v1
2402.10754 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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T04:54:36.175377Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T15:26:11.141418Z

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 c0052a1a-ecbb-423c-a4fe-6bd031eabc44 · inbound

Semia: Auditing Agent Skills via Constraint-Guided Representation Synthesis cites this paper.

Semia: Auditing Agent Skills via Constraint-Guided Representation Synthesis LLMDFA: Analyzing Dataflow in Code with Large Language Models

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:26:11.216670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-09T19:58:28.584941Z digest=sha256:e30c7369772e1b4dbd1aef4faa18519ba8e1724fa98387b7a59352b72f913e16

Observation 6a86c6aa-24c2-4212-b061-823682aa414f · inbound

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors via Adversarial Code Comments cites this paper.

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors via Adversarial Code Comments LLMDFA: Analyzing Dataflow in Code with Large Language Models

Reference 56

Resolution
unresolved
no resolver link, observed 2026-07-31T04:54:36.175377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T04:54:36.175377Z digest=sha256:b94a97e064bce5d2a7200d26502b739e8dcd957942fe1c3cf26c0209ee3440b8