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Paper Citation Record · LEDGER

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 8 inbound Pith citation observations for arXiv:2505.19828.

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

pith.paper-citation-record.v1
2505.19828 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:09:03.342895Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:02:54.227103Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:09:37.546449Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact1
  • verified fuzzy14
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7fda9ac4-0923-414d-b5fd-af4fc7bd09ed · outbound

This paper cites Cvefixes: automated collection of vulner- abilities and their fixes from open-source software.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Cvefixes: automated collection of vulner- abilities and their fixes from open-source software

Reference 1

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raw_fallback, observed 2026-08-07T14:09:09.164889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.065583Z digest=sha256:9cd213895bb3adb06ed643a8c8c4fc1318d555445853107a7675306df7d08362

Observation 0b31b96f-8165-4ee0-b0e7-45dccea377f1 · outbound

This paper cites Deep learning based vulnerability detection: Are we there yet? IEEE Transactions on Software Engineering , 48(9):3280–3296, 2021.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Deep learning based vulnerability detection: Are we there yet? IEEE Transactions on Software Engineering , 48(9):3280–3296, 2021

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.201054Z digest=sha256:edb0619ba96c62e65b5c16f04f675eaf47b6d4fe3f041eb75a32e58e748ed5b2

Observation 7af37375-ecc7-4c59-8294-3ad0377109a7 · outbound

This paper cites Diversevul: A new vulnerable source code dataset for deep learning based vulnerability detection.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Diversevul: A new vulnerable source code dataset for deep learning based vulnerability detection

Reference 3

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raw_fallback, observed 2026-08-07T14:09:08.294753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.324789Z digest=sha256:27f3e38d4970728a729dcfd2557a0b4293b95e21905666dd33e5345815528e00

Observation 15710a1b-3497-45ba-82dc-51d0a0712e80 · outbound

This paper cites CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation

Reference 4

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local_arxiv, observed 2026-08-07T14:09:04.509343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.394753Z digest=sha256:653343e6d0c64a86de2eeac071dc8e28defc9b2a3282cbac6cad7b254c40b20a

Observation e9dba13b-230c-47a9-b8e2-150acbba3834 · outbound

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

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Vulnerability Detection with Code Language Models: How Far Are We?

Reference 5

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no resolver link, observed 2026-08-07T14:09:01.505613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:01.505613Z digest=sha256:538b9e9806271e0e43abe9e088681127bf03dbedcd399d933d6cda58452cb552

Observation a142ed5f-90e0-425c-824c-05ee848a7b91 · outbound

This paper cites Ac/c++ code vulnerability dataset with code changes and cve summaries.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Ac/c++ code vulnerability dataset with code changes and cve summaries

Reference 6

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raw_fallback, observed 2026-08-07T14:09:08.014836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.594912Z digest=sha256:785370f2d42b34500be4cd85f355304bb0fe16b0d1e1133c75d0bc0d60cad1bb

Observation 36c11f08-37e7-42c7-ae59-f38cec9c8fe8 · outbound

This paper cites Linevul: A transformer-based line-level vulner- ability prediction.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Linevul: A transformer-based line-level vulner- ability prediction

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.704620Z digest=sha256:500c472cd3b7cb10f8263207f2da05f11c0ed385e0ee2d11aefa4d7484933b82

Observation be94fd98-fe0f-4a57-907a-0b6fc8cabccb · outbound

This paper cites Binaiv: Semantic-enhanced vulnerability detection for linux x86 binaries.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Binaiv: Semantic-enhanced vulnerability detection for linux x86 binaries

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.784748Z digest=sha256:cf81938de5da915b88b5ec4476236d461e5f28da605d94fdf732b4b8c4186a9c

Observation 2bd7db58-b990-4ae2-95fa-b141af86558a · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:01.864943Z digest=sha256:af46d232d2d7a76dc984e58a33e675a1b4aaa78becc60076ef62137871788bc3

Observation 5a9ab519-fe7b-4498-9651-f58b8a834c94 · outbound

This paper cites Outside the comfort zone: Analysing llm capabilities in software vulnerability detection.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Outside the comfort zone: Analysing llm capabilities in software vulnerability detection

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:01.960237Z digest=sha256:e025123f81e8de81c25fd0eb1f152f268a55f9cf6770edbf57e6f088bfe7a54c

Observation 08f52629-b115-4262-a17b-41d1b2701d98 · outbound

This paper cites Large language models for code: Security hardening and adversarial testing.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Large language models for code: Security hardening and adversarial testing

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:02.015105Z digest=sha256:b82c8e785a8b2466681b444ff99fb3b34a579e220b740ff2ed9dfee4ac7dcd66

Observation 13daf1a2-e200-4387-8905-19c2ee26de1f · outbound

This paper cites Linevd: Statement-level vulnerability detection using graph neural networks.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Linevd: Statement-level vulnerability detection using graph neural networks

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:02.093488Z digest=sha256:1f9931a153b39c425c1be9f901fbce18493ef3739d22d8b22d28df6847042727

Observation 3f6f2760-a18a-4590-9520-18a7ebaee44d · outbound

This paper cites Qwen2.5-Coder Technical Report.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Qwen2.5-Coder Technical Report

Reference 13

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no resolver link, observed 2026-08-07T14:09:02.194848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:02.194848Z digest=sha256:a7ea8459ac080749c86365563e1b91a75c77eb8967948e89d1afd6f0d9d22d4d

Observation 6549ba68-065e-4805-aa9a-176e45492013 · outbound

This paper cites LLM-Assisted Code Cleaning For Training Accurate Code Generators.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection LLM-Assisted Code Cleaning For Training Accurate Code Generators

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:02.284613Z digest=sha256:cfcbfa167193ad4a7a96ca3c45b4133f33dae4e2894a83e3318bc0e4d13d611d

Observation 7f89d785-a6b7-4ef9-bc34-8760e80d1d64 · outbound

This paper cites Mistral 7B.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Mistral 7B

Reference 15

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no resolver link, observed 2026-08-07T14:09:02.368873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:02.368873Z digest=sha256:ff0ab4d0581c38f966fbf868711baa066ca0d1db1e34ea187cb692c826a6fda2

Observation a4eb7679-4220-49fe-bab5-e904417d0a04 · outbound

This paper cites More Agents Is All You Need.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection More Agents Is All You Need

Reference 16

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no resolver link, observed 2026-08-07T14:09:02.474889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:02.474889Z digest=sha256:b0d279a00bd6754df0a787523f9630fa7e4fd1adc9139005a3cd8125cf3c48c3

Observation ab700f1e-27a7-4a03-9450-28d18178a287 · outbound

This paper cites Vulnerability management in linux distributions: An empirical study on debian and fedora.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Vulnerability management in linux distributions: An empirical study on debian and fedora

Reference 17

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raw_fallback, observed 2026-08-07T14:09:06.335487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:02.563821Z digest=sha256:11b3ec9aaaeef7d27aac426cae08805b1f2b6192e5adb5544b825c576ab0732b

Observation 2a66c58d-653c-4790-8777-522b90363c26 · outbound

This paper cites Megavul: Ac/c++ vulnerability dataset with comprehensive code representations.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Megavul: Ac/c++ vulnerability dataset with comprehensive code representations

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:02.614753Z digest=sha256:c8514177ff36bdae12c8eef0aae4e93a99164568f76566e0fc40f02613ee51cd

Observation b4a8d715-be84-4840-93d7-b4371af59214 · outbound

This paper cites Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:02.715563Z digest=sha256:b18a3b9fca9ae5b70e23fa00787c3bbfe316933914838bd53f5a0d075b734668

Observation c5a5f61a-649d-4a01-81ba-c324576356b9 · outbound

This paper cites LProtector: An LLM-driven Vulnerability Detection System.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection LProtector: An LLM-driven Vulnerability Detection System

Reference 20

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:02.793299Z digest=sha256:2bb63acd821f298e9ed358efba8c05a8379be7f93147b3efbdc05600cd272bec

Observation 4e542395-0863-46cf-ab38-0d69c41d9554 · outbound

This paper cites A systematic literature review on automated software vulnerability detection using machine learning.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection A systematic literature review on automated software vulnerability detection using machine learning

Reference 21

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raw_fallback, observed 2026-08-07T14:09:05.849365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:02.875131Z digest=sha256:f5ea6b1ed5276b0b9fc9c27104050c3f74315f5645e1b08ebb6846dca94771b3

Observation 112e7eb0-bc16-4d0e-b88b-440d12f733bc · outbound

This paper cites Simulating strategic reasoning: Comparing the ability of single llms and multi-agent systems to replicate human behavior.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Simulating strategic reasoning: Comparing the ability of single llms and multi-agent systems to replicate human behavior

Reference 22

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raw_fallback, observed 2026-08-07T14:09:05.551887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:02.994750Z digest=sha256:67afc1206c2401c9c79ca87b67133a421c7413fd5df872b2a5f20c3365a7161e

Observation 7b150650-86db-44b3-831b-6489232f5ae6 · outbound

This paper cites Multi-Agent Collaboration Mechanisms: A Survey of LLMs.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Multi-Agent Collaboration Mechanisms: A Survey of LLMs

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:09:03.185290Z digest=sha256:b08ca2a8966ce058cc069c4e15d8060f17b6a97c779b7d61bc3581f925e529b0

Observation 5cd071fa-e9dc-4e35-ae9f-8de37f454c38 · outbound

This paper cites Large language model for vulnerability detection: Emerging results and future directions.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection Large language model for vulnerability detection: Emerging results and future directions

Reference 24

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raw_fallback, observed 2026-08-07T14:09:05.054750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:03.264826Z digest=sha256:f5ada17a5a3a3ee06f8e596d2b70e10df87eea919f37cb36387c5119cc32d074

Observation 2fdf72a3-35f0-4c4c-82d7-c9b29e4a4767 · outbound

This paper cites relevant context.

SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection relevant context

Reference 25

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raw_fallback, observed 2026-08-07T14:09:04.774749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:09:03.342895Z digest=sha256:290189a112cc09aedbc10fe097d92f7942301978e6759b082d7452a366c00316

Pith citing papers

Observation 3335d6c9-997a-4a55-abf1-39b619b0fd84 · inbound

VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities cites this paper.

VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 3

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:02:54.227103Z digest=sha256:aba1b00920b19df12b0d95107a8fcf6bf8429a9e4ba6782daed132b6c634f236

Observation d50d42f2-d11b-4ea7-a370-ba0edb90fc77 · inbound

VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization cites this paper.

VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:12:31.417987Z digest=sha256:3cc2e72d7c78f2914cf2d6822ac48be60ebcf474fd948226f68615f7aaad22d2

Observation 2bb33fad-bcb2-44c6-abc3-d32b463dfadd · inbound

ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage cites this paper.

ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 2

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arxiv_id, observed 2026-05-22T09:41:21.777646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T09:40:21.746161Z digest=sha256:fb91c1e0fe74892ff749a6069845833e5a78ffd47dcaf2631f1cd62e6a562070

Observation edaebdec-b228-4a68-ad2f-a58ad29127ba · inbound

Needles at Scale: LLM-Assisted Target Selection for Windows Vulnerability Research cites this paper.

Needles at Scale: LLM-Assisted Target Selection for Windows Vulnerability Research SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 1

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arxiv_id, observed 2026-07-01T21:36:14.802704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T16:48:15.968088Z digest=sha256:b90040d6134bbabd0dd2e0cd9f0f5816ad9e193dc7637b7c78cbb7cb54a739c4

Observation 72810641-d066-46c2-893a-5cf1c42f3274 · inbound

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation cites this paper.

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 24

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arxiv_id, observed 2026-07-03T21:58:59.103098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T23:56:34.841978Z digest=sha256:5d224e27d16e71ae9136628670a43132cd8d5e013327664e2915f894c2332611

Observation 07613a3b-58b9-479a-b8a6-5a7d806cb29e · inbound

Evaluating LLMs for Real-World Web Vulnerability Detection cites this paper.

Evaluating LLMs for Real-World Web Vulnerability Detection SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 1

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arxiv_id, observed 2026-07-04T07:09:37.549787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T13:50:13.974457Z digest=sha256:73137460cb0b0780d2aaf4458385b83e8202cf86f9c7c7c69bd4048673689aea

Observation 01bef7ce-6348-48a5-90fb-c49ec924f19b · inbound

Neuro-Symbolic Reasoning for Vulnerability Detection cites this paper.

Neuro-Symbolic Reasoning for Vulnerability Detection SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 2

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no resolver link, observed 2026-07-11T22:42:57.965190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T22:42:57.965190Z digest=sha256:c06b3b1ba4481279db195c2cf18651892b7065d63ccc518e7045f8e7b2f497ca

Observation a38550a7-ed79-44c2-b4fb-c585beeb62e6 · inbound

DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection cites this paper.

DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:13:49.632959Z digest=sha256:f273b72257471f841d6728a8d9104e6c62fd21644fad2df7537e33911560cc15