Paper Citation Record · LEDGER

A Mixture of Linear Corrections Generates Secure Code

As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.09508.

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

pith.paper-citation-record.v1
2507.09508 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:59:05.417115Z

measured 47 of 47 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 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

47 of 47 outbound references displayed

verified exact1
verified fuzzy29
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 896d4d52-fbd5-4f47-81f1-ce29ea2578c6 · outbound

This paper cites GitHub CodeQL, 2025.https://github.com/github/codeql.

A Mixture of Linear Corrections Generates Secure Code GitHub CodeQL, 2025.https://github.com/github/codeql

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:13.154059Z

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 9c515621-e5ac-4977-a87f-92c132898a7b · outbound

This paper cites Controllable Text Generation for Large Language Models: A Survey.

A Mixture of Linear Corrections Generates Secure Code Controllable Text Generation for Large Language Models: A Survey

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:00.483831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7224fe24-699b-4d49-beda-00ff9ed13f20 · outbound

This paper cites Generalization- enhanced code vulnerability detection via multi-task instruction fine-tuning.

A Mixture of Linear Corrections Generates Secure Code Generalization- enhanced code vulnerability detection via multi-task instruction fine-tuning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:12.963006Z

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 ff358b16-aa5b-458b-b2d1-7935a38c2f4b · outbound

This paper cites Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models.

A Mixture of Linear Corrections Generates Secure Code Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:00.738847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ec424ac-24c0-4f19-9dcb-7f8699a78e7e · outbound

This paper cites Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions.

A Mixture of Linear Corrections Generates Secure Code Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:12.688198Z

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 2071d461-85db-4195-96a8-749de5c58c77 · outbound

This paper cites Doccgen: Document-based controlled code generation.

A Mixture of Linear Corrections Generates Secure Code Doccgen: Document-based controlled code generation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:12.394628Z

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 72e6f85d-c177-42ba-a281-8533ae713c5b · outbound

This paper cites Vul-rag: Enhancing llm-based vulnerability detection via knowledge-level rag.

A Mixture of Linear Corrections Generates Secure Code Vul-rag: Enhancing llm-based vulnerability detection via knowledge-level rag

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:12.054916Z

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 930a469f-19ba-46c7-bb92-b9fb35adfd3b · outbound

This paper cites Representation engineering: A top-down approach to ai transparency.CoRR, 2023.

A Mixture of Linear Corrections Generates Secure Code Representation engineering: A top-down approach to ai transparency.CoRR, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:11.772223Z

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 d94ed009-98d2-407f-933a-f314059286dc · outbound

This paper cites Taxonomy, opportunities, and challenges of representation engineering for large language models.arXiv preprint arXiv:2502.19649, 2025.

A Mixture of Linear Corrections Generates Secure Code Taxonomy, opportunities, and challenges of representation engineering for large language models.arXiv preprint arXiv:2502.19649, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:01.299021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd0c4da6-d142-49cd-b546-42b4c493fe73 · outbound

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

A Mixture of Linear Corrections Generates Secure Code Vulnerability Detection with Code Language Models: How Far Are We?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:01.461184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8333f53d-a725-4378-a4f7-e78b60d2836f · outbound

This paper cites Vuldebert: A vulnerability detection system using bert.

A Mixture of Linear Corrections Generates Secure Code Vuldebert: A vulnerability detection system using bert

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:11.548579Z

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 1e467301-e1ac-404b-8e6f-80da4d1d7322 · outbound

This paper cites Assbert: Active and semi- supervised bert for smart contract vulnerability detection.Journal of Information Security and Applications, 73:103423, 2023.

A Mixture of Linear Corrections Generates Secure Code Assbert: Active and semi- supervised bert for smart contract vulnerability detection.Journal of Information Security and Applications, 73:103423, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:11.305428Z

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 9e1ca147-9077-4b07-8ee4-637faace2406 · outbound

This paper cites Vulrepair: a t5- based automated software vulnerability repair.

A Mixture of Linear Corrections Generates Secure Code Vulrepair: a t5- based automated software vulnerability repair

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:11.031701Z

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 0eb9872a-c302-43f8-988e-428410c97439 · outbound

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

A Mixture of Linear Corrections Generates Secure Code Large language model for vulnerability detection: Emerging results and future directions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:09.438800Z

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 4487efbe-ab1f-434e-8956-a2427d5d7827 · outbound

This paper cites The Internal State of an LLM Knows When It's Lying.

A Mixture of Linear Corrections Generates Secure Code The Internal State of an LLM Knows When It's Lying

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:02.063190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c246acf3-9467-4602-bb1d-1915545899ee · outbound

This paper cites States Hidden in Hidden States: Implicit Discrete State Representations Emerge in LLMs' Hidden States.

A Mixture of Linear Corrections Generates Secure Code States Hidden in Hidden States: Implicit Discrete State Representations Emerge in LLMs' Hidden States

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:02.183060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e26062ed-13af-4983-abee-2b0903d58599 · outbound

This paper cites Towards inference-time category-wise safety steering for large language models.

A Mixture of Linear Corrections Generates Secure Code Towards inference-time category-wise safety steering for large language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:09.185319Z

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 cec7c475-8b4d-45ce-9ec9-831f70bf3686 · outbound

This paper cites Steering llama 2 via contrastive activation addition.

A Mixture of Linear Corrections Generates Secure Code Steering llama 2 via contrastive activation addition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:02.386456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a694497-3c19-44e2-adc3-6a1ddc6c8049 · outbound

This paper cites Challenges with applying vulnera- bility prediction models.

A Mixture of Linear Corrections Generates Secure Code Challenges with applying vulnera- bility prediction models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:08.996061Z

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 18f6f870-2f06-4fc5-a758-0cab3a964960 · outbound

This paper cites Do bugs foreshadow vulnerabilities? a study of the chromium project.

A Mixture of Linear Corrections Generates Secure Code Do bugs foreshadow vulnerabilities? a study of the chromium project

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:08.824201Z

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 1d06e738-1934-4a4f-87e4-ca4f79f538e6 · outbound

This paper cites Chatgpt for vulnerability detection, classification, and repair: How far are we? In2023 30th Asia-Pacific Software Engineering Conference (APSEC), pages 632–636.

A Mixture of Linear Corrections Generates Secure Code Chatgpt for vulnerability detection, classification, and repair: How far are we? In2023 30th Asia-Pacific Software Engineering Conference (APSEC), pages 632–636

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:08.684165Z

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 461e9226-8caa-4907-af27-06e3cf380441 · outbound

This paper cites Llms cannot reliably identify and reason about security vulnerabilities (yet?): A comprehensive evaluation, framework, and benchmarks.

A Mixture of Linear Corrections Generates Secure Code Llms cannot reliably identify and reason about security vulnerabilities (yet?): A comprehensive evaluation, framework, and benchmarks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:08.471213Z

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 baaa13b1-521f-4afc-a317-68b0be8eba07 · outbound

This paper cites Enhancing static analysis for practical bug detection: An llm-integrated approach.Proceedings of the ACM on Programming Languages, 8(OOPSLA1):474–499, 2024.

A Mixture of Linear Corrections Generates Secure Code Enhancing static analysis for practical bug detection: An llm-integrated approach.Proceedings of the ACM on Programming Languages, 8(OOPSLA1):474–499, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:08.354584Z

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 3aff9639-47eb-47c7-9a33-64168d3d7f3d · outbound

This paper cites LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning.

A Mixture of Linear Corrections Generates Secure Code LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:03.161302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20c36818-6a39-4012-83dd-985f697223e8 · outbound

This paper cites Instruction tuning for secure code generation.

A Mixture of Linear Corrections Generates Secure Code Instruction tuning for secure code generation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:08.210621Z

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 77aea52d-f7a4-4ffa-bab0-4e7d8c5f5c27 · outbound

This paper cites ProSec: Fortifying Code LLMs with Proactive Security Alignment.

A Mixture of Linear Corrections Generates Secure Code ProSec: Fortifying Code LLMs with Proactive Security Alignment

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:03.421167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcdfad5b-2cb9-45b0-bbff-797b8bb21ba4 · outbound

This paper cites APILOT: Navigating Large Language Models to Generate Secure Code by Sidestepping Outdated API Pitfalls.

A Mixture of Linear Corrections Generates Secure Code APILOT: Navigating Large Language Models to Generate Secure Code by Sidestepping Outdated API Pitfalls

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:59:05.681274Z

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 b2fee65f-4fd7-45e8-aa4d-b796af7130a5 · outbound

This paper cites Indict: Code generation with internal dialogues of critiques for both security and helpfulness.

A Mixture of Linear Corrections Generates Secure Code Indict: Code generation with internal dialogues of critiques for both security and helpfulness

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:08.042006Z

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 af50e917-27b9-45f7-a918-8cd64258dbb5 · outbound

This paper cites Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities.

A Mixture of Linear Corrections Generates Secure Code Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:03.805856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18cdc02d-4acd-46fb-8f9e-812bbee1aff9 · outbound

This paper cites Learning Code Preference via Synthetic Evolution.

A Mixture of Linear Corrections Generates Secure Code Learning Code Preference via Synthetic Evolution

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:03.902262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1489ea8-052d-49dc-a89d-2c849d7c7f39 · outbound

This paper cites Per- sonalized steering of large language models: Versatile steering vectors through bi-directional preference optimization.

A Mixture of Linear Corrections Generates Secure Code Per- sonalized steering of large language models: Versatile steering vectors through bi-directional preference optimization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:07.937595Z

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 1cf1e60c-4824-4ba5-8490-ed99fd959c71 · outbound

This paper cites Adaptive activation steering: A tuning-free llm truthfulness improvement method for diverse hallucinations categories.

A Mixture of Linear Corrections Generates Secure Code Adaptive activation steering: A tuning-free llm truthfulness improvement method for diverse hallucinations categories

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:04.109582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d44e9fb7-69d5-4fee-be4a-5f9b9fb58165 · outbound

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

A Mixture of Linear Corrections Generates Secure Code Large language models for code: Security hardening and adversarial testing

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:07.773862Z

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 465da816-578c-4f3e-8479-c0510baee347 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

A Mixture of Linear Corrections Generates Secure Code Evaluating Large Language Models Trained on Code

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:04.252729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de80201d-ce54-4e35-a4da-5dcd56153b81 · outbound

This paper cites Codegeex: A pre-trained model for code generation with multilingual benchmarking on humaneval-x.

A Mixture of Linear Corrections Generates Secure Code Codegeex: A pre-trained model for code generation with multilingual benchmarking on humaneval-x

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:07.601847Z

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 81e2c13a-0999-4058-967f-5dee86f8e36a · outbound

This paper cites Solving quantitative reasoning problems with language models.Advances in Neural Information Processing Systems, 35:3843–3857, 2022.

A Mixture of Linear Corrections Generates Secure Code Solving quantitative reasoning problems with language models.Advances in Neural Information Processing Systems, 35:3843–3857, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:07.467454Z

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 c9fd565b-d1ff-4b89-965d-2074e76ba38b · outbound

This paper cites A Survey on Large Language Models for Code Generation.

A Mixture of Linear Corrections Generates Secure Code A Survey on Large Language Models for Code Generation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:04.409540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19a5526a-e9b5-4410-90e7-d36ac01c09c0 · outbound

This paper cites Qwen2.5-Coder Technical Report.

A Mixture of Linear Corrections Generates Secure Code Qwen2.5-Coder Technical Report

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:04.483089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffb5676f-b07e-4761-beda-1643583dd97d · outbound

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

A Mixture of Linear Corrections Generates Secure Code Code Llama: Open Foundation Models for Code

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T17:59:04.556485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10dfc869-87b0-434a-af37-35f73905acf6 · outbound

This paper cites Understanding intermediate layers using linear classifier probes, 2017.

A Mixture of Linear Corrections Generates Secure Code Understanding intermediate layers using linear classifier probes, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:07.343801Z

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 a351bde5-f51a-484f-8906-dec71ad5d184 · outbound

This paper cites Linevul: A transformer-based line-level vulnerability pre- diction.

A Mixture of Linear Corrections Generates Secure Code Linevul: A transformer-based line-level vulnerability pre- diction

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:07.028748Z

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 ffb34d9c-f407-48bf-bc57-d6900c0a7887 · outbound

This paper cites Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks.Advances in neural information processing systems, 32, 2019.

A Mixture of Linear Corrections Generates Secure Code Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks.Advances in neural information processing systems, 32, 2019

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:06.857487Z

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 2da5f09f-8f52-4228-b7dd-7e38f8fa2b0f · outbound

This paper cites And the CodeLlama series tend to regard the CWE-416, CWE-476 and CWE-787 as safe, as in Table 12 and Table 13.

A Mixture of Linear Corrections Generates Secure Code And the CodeLlama series tend to regard the CWE-416, CWE-476 and CWE-787 as safe, as in Table 12 and Table 13

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:06.675557Z

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 1a1c23c1-1b6a-4563-923d-ee2a068cea63 · outbound

This paper cites And overall, the QC series show a better instruction following ability than CL series, as the Invalid rates are lower.

A Mixture of Linear Corrections Generates Secure Code And overall, the QC series show a better instruction following ability than CL series, as the Invalid rates are lower

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:06.509600Z

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 e94f3390-70d6-4b40-bfbd-0855813c05e3 · outbound

This paper cites Possible reasons are that the PCA reduced too much information that may be essential for vulnerability detection.

A Mixture of Linear Corrections Generates Secure Code Possible reasons are that the PCA reduced too much information that may be essential for vulnerability detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:06.367616Z

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 df49b5a0-f986-4dda-acda-ba6c6915dce8 · outbound

This paper cites And these shows a higher accuracy than other CWEs, especially on QC-14B and 7B models.

A Mixture of Linear Corrections Generates Secure Code And these shows a higher accuracy than other CWEs, especially on QC-14B and 7B models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:59:06.200847Z

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 7028601d-43fe-4ca1-a3fa-10c30ca40152 · outbound

This paper cites an unresolved cited work.

A Mixture of Linear Corrections Generates Secure Code Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:59:06.060260Z

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.

Pith citing papers

No inbound Pith citation observations are available.