Paper Citation Record · LEDGER

Exploiting Novel GPT-4 APIs

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2312.14302.

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

pith.paper-citation-record.v1
2312.14302 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:53:39.222002Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T08:58:10.420842Z

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 939af47c-6caa-44b8-9f3e-de1fbbc1137d · inbound

Refusal in Language Models Is Mediated by a Single Direction cites this paper.

Refusal in Language Models Is Mediated by a Single Direction Exploiting Novel GPT-4 APIs

Reference 171

Resolution: verified exact; arxiv_id, observed 2026-05-13T10:47:56.075841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 279389d1-46d6-46c5-bbdf-e9f12da5fd2c · inbound

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents cites this paper.

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents Exploiting Novel GPT-4 APIs

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-14T01:35:51.297421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d817377a-7633-492d-bcb6-2cb54874f2f0 · inbound

Veracity: An Open-Source AI Fact-Checking System cites this paper.

Veracity: An Open-Source AI Fact-Checking System Exploiting Novel GPT-4 APIs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T23:53:39.222002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d72aad72-62ff-46d8-b916-81bc4d105fe5 · inbound

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation cites this paper.

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation Exploiting Novel GPT-4 APIs

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T20:31:36.707987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 432c1e46-1711-4b7e-a492-a3a2985bd433 · inbound

Understanding the Effects of Safety Unalignment on Large Language Models cites this paper.

Understanding the Effects of Safety Unalignment on Large Language Models Exploiting Novel GPT-4 APIs

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:38:14.481152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 73160a6e-ff7b-4e7f-afc7-d9b90af5abee · inbound

AttackEval: A Systematic Empirical Study of Prompt Injection Attack Effectiveness Against Large Language Models cites this paper.

AttackEval: A Systematic Empirical Study of Prompt Injection Attack Effectiveness Against Large Language Models Exploiting Novel GPT-4 APIs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T12:52:49.101462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d67191d-ae64-4320-8053-8208c997b069 · inbound

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks cites this paper.

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks Exploiting Novel GPT-4 APIs

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:58:10.422181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.