Paper Citation Record · LEDGER

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data

As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 5 inbound Pith citation observations for arXiv:2506.07390.

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

pith.paper-citation-record.v1
2506.07390 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:41:54.438531Z

measured 21 of 21 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T16:30:08.370733Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T03:38:40.346052Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy4
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9b2ba49-b2dd-4a12-a2b5-236098d73365 · outbound

This paper cites Specifically, line 16 of the target code does not check if the input tensors are empty before proceeding with the division.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Specifically, line 16 of the target code does not check if the input tensors are empty before proceeding with the division

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:54.575335Z

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.

Observation b657ef3c-2add-4b3f-b8d0-6bf6453fb107 · outbound

This paper cites an unresolved cited work.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:54.621021Z

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.

Observation 42e262f3-6e04-4b33-a04b-c5df82dcb1bd · outbound

This paper cites an unresolved cited work.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:54.550783Z

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.

Observation c7a930e1-bf6b-4a43-906b-3beaa7f95581 · outbound

This paper cites Vulnerability Detection with Code Language Models: How Far Are We?.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Vulnerability Detection with Code Language Models: How Far Are We?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:54.393834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86b0384a-97c1-4690-a3b7-b85f4fa301d2 · outbound

This paper cites an unresolved cited work.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:54.609320Z

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.

Observation 033ea37f-0e7d-42bb-a9c5-d0760582bf2d · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data StarCoder 2 and The Stack v2: The Next Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:54.402035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69c4922f-f1bb-4af4-a832-8b3c213423cc · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Code Llama: Open Foundation Models for Code

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:54.409621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b6c68bc-d79a-4f9c-bf4d-f874ca7206b1 · outbound

This paper cites In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions), Bangkok, Thailand.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions), Bangkok, Thailand

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:54.586907Z

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.

Observation faf4a35a-4fe0-4c9c-a90c-5b019527d46e · outbound

This paper cites Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:54.417624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68785c43-0860-4f4d-9ef9-d7a152aafbe3 · outbound

This paper cites The control flow includes checking the type of the output tensor and then calling the appropriate evaluation function.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data The control flow includes checking the type of the output tensor and then calling the appropriate evaluation function

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:54.562809Z

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.

Observation 076342ea-a96c-4b84-9e12-f5b43e4f3c61 · outbound

This paper cites an unresolved cited work.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:54.539726Z

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.

Observation 60ffc98b-d769-47e9-a462-192174e2ccaf · outbound

This paper cites The control flow contains a check for the output tensor type and then a conditional check for the input tensor type.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data The control flow contains a check for the output tensor type and then a conditional check for the input tensor type

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:54.528808Z

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.

Observation 6fc87ccf-36e4-47a5-810f-13d5945a1b3a · outbound

This paper cites an unresolved cited work.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:54.517507Z

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.

Observation 1c3f6493-598d-4e2e-acbd-e6161b71fe74 · outbound

This paper cites an unresolved cited work.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:54.597250Z

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.

Observation 842f0d7a-5dad-44c7-a0e0-d3fc1a9006d7 · outbound

This paper cites an unresolved cited work.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:54.632450Z

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.

Observation 9c436822-d943-4e1f-a1c2-924f170c08be · outbound

This paper cites MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models.

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models

Reference 4455

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:54.389863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4aa59be4-e05f-466d-b81e-7351045486d5 · inbound

VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection cites this paper.

VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:21:04.292017Z

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.

Observation 17ef13a1-bb4a-4eb2-a3bd-8e02bd6549c6 · inbound

VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection cites this paper.

VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T16:30:08.370733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2b428f8-a4b6-410d-aa20-14693a3edbaf · inbound

Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries cites this paper.

Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:05:54.708635Z

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.

Observation 65acd04a-2abb-457a-bcf3-901e33d5dcdb · inbound

Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries cites this paper.

Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-12T16:46:36.842198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08a9fa86-752d-4604-b350-f4b4a307f1c2 · inbound

Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries cites this paper.

Veritas: Grounding LLM Agents for Reliable Vulnerability Reasoning over Stripped Binaries Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-02T14:03:39.021192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.