Paper Citation Record · LEDGER

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2607.21069.

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

pith.paper-citation-record.v1
2607.21069 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:38:08.870384Z

measured 39 of 39 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

39 of 39 outbound references displayed

verified exact1
verified fuzzy0
unresolved37
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malformed identifier1
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External citation measurements

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Outbound references

Observation c098d661-7009-4c75-8365-e02f61e76dfb · outbound

This paper cites Adaptive Hierarchical Evaluation of LLMs and SAST tools for CWE Prediction in Python.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Adaptive Hierarchical Evaluation of LLMs and SAST tools for CWE Prediction in Python

Reference 1

Resolution: malformed identifier; no resolver link, observed 2026-08-01T08:38:04.187051Z

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Unavailable: canonical work link unavailable.

Observation df706bba-9282-4733-90e0-7a4df9893182 · outbound

This paper cites Measuring and mitigating debugging effectiveness decay in code language models.Scientific Reports2025,15, 44120.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Measuring and mitigating debugging effectiveness decay in code language models.Scientific Reports2025,15, 44120

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:04.409628Z

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Observation 0ee93be2-fc3d-43f6-af62-3c2abae2791c · outbound

This paper cites Large Language Model Guided Self-Debugging Code Generation.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Large Language Model Guided Self-Debugging Code Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:04.533521Z

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Observation dd812e42-5b04-48bf-8530-a4838811516d · outbound

This paper cites Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions.Commun.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions.Commun

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:04.661465Z

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Unavailable: canonical work link unavailable.

Observation 3aae3826-81ab-40b9-a5b8-5b9aef257072 · outbound

This paper cites How secure is AI-generated code: A large-scale comparison of large language models.Empirical Software Engineering2025,30, 47.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python How secure is AI-generated code: A large-scale comparison of large language models.Empirical Software Engineering2025,30, 47

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:04.792435Z

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Unavailable: canonical work link unavailable.

Observation 4984b264-2470-4d3c-9453-bc1ce9703f09 · outbound

This paper cites When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs.Transactions of the Association for Computational Linguistics2024,12, 1417–1440.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs.Transactions of the Association for Computational Linguistics2024,12, 1417–1440

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:04.920900Z

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Observation 29ec5ae5-38e5-4387-b6ea-cbfb218669c5 · outbound

This paper cites SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.029909Z

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Unavailable: canonical work link unavailable.

Observation 3d40ac4f-b6a8-48ec-803c-16e2b88acf17 · outbound

This paper cites CVEfixes: automated collection of vulnerabilities and their fixes from open-source software.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python CVEfixes: automated collection of vulnerabilities and their fixes from open-source software

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.178210Z

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Unavailable: canonical work link unavailable.

Observation 8b69bb67-7fe0-402b-a4fd-759253b3d9f8 · outbound

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

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Large language models for code: Security hardening and adversarial testing

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.327960Z

Source-reported events for the cited work

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Observation cfbf668d-89d0-47a0-98cd-acedc35a0b15 · outbound

This paper cites Instruction tuning for secure code generation.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Instruction tuning for secure code generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.398840Z

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Observation ca313e9d-ac1b-46f5-b46b-b92e6aab55e6 · outbound

This paper cites Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.463593Z

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Observation c78a4f21-9e28-4b29-97d1-4485e3805825 · outbound

This paper cites DLAP: A Deep Learning Augmented Large Language Model Prompting framework for software vulnerability detection.J.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python DLAP: A Deep Learning Augmented Large Language Model Prompting framework for software vulnerability detection.J

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.566702Z

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Observation 403913c3-bb50-4ac7-ada8-242f47dadd9c · outbound

This paper cites An Empirical Study of Vulnerabilities in Python Packages and Their Detection.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python An Empirical Study of Vulnerabilities in Python Packages and Their Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.635757Z

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Observation 9c2eb6c4-6ea2-427e-85fc-83b15a90bfc1 · outbound

This paper cites Common weakness enumeration (CWE) status update.Ada Lett.2008, XXVIII, 88–91.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Common weakness enumeration (CWE) status update.Ada Lett.2008, XXVIII, 88–91

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.737415Z

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Observation 197aa59f-45ec-466b-a38f-1d3a44992b79 · outbound

This paper cites Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:05.863362Z

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Observation 64ed8b8e-eff8-4eba-9892-18173fc463ff · outbound

This paper cites Deep reinforcement learning from human preferences.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Deep reinforcement learning from human preferences

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:06.050163Z

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Observation edf1e5de-a18b-4fce-9c79-57883ad9a5c8 · outbound

This paper cites Training language models to follow instructions with human feedback.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Training language models to follow instructions with human feedback

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:06.213665Z

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Unavailable: canonical work link unavailable.

Observation 4e63ef3b-fd13-4619-8f4b-48b6af32e043 · outbound

This paper cites Proximal Policy Optimization Algorithms.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Proximal Policy Optimization Algorithms

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:06.362882Z

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Observation 9447bed3-0018-4279-b667-bf35e7c5bda6 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:06.551703Z

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Observation a2d30cef-cb17-45af-adaf-ffde20301c91 · outbound

This paper cites CodeRL: mastering code generation through pretrained models and deep reinforcement learning.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python CodeRL: mastering code generation through pretrained models and deep reinforcement learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:06.701822Z

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Observation 80022d76-fdfa-4315-a37c-8b63187432a6 · outbound

This paper cites Execution-based Code Generation using Deep Reinforcement Learning.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Execution-based Code Generation using Deep Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:06.831366Z

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Observation 030502de-bde9-406a-8294-6791e3012fef · outbound

This paper cites RLTF: Reinforcement Learning from Unit Test Feedback.Transactions on Machine Learning Research2023.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python RLTF: Reinforcement Learning from Unit Test Feedback.Transactions on Machine Learning Research2023

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:06.972978Z

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Observation 65eba85a-dc2c-4682-8d9e-69592667da87 · outbound

This paper cites Stepcoder: improving code generation with reinforcement learning from compiler feedback.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Stepcoder: improving code generation with reinforcement learning from compiler feedback

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.053196Z

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Observation ef202044-c009-47f8-9fb2-e4c89567e029 · outbound

This paper cites LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.173572Z

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Unavailable: canonical work link unavailable.

Observation f50a9149-319a-4980-9893-6ec33a27b6a9 · outbound

This paper cites Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.245761Z

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Observation d173f320-40d4-45f3-84e9-0d35d06a6ffe · outbound

This paper cites Secure Code Generation via On- line Reinforcement Learning with Vulnerability Reward Model, 2026, [arXiv:cs.CR/2602.07422].

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Secure Code Generation via On- line Reinforcement Learning with Vulnerability Reward Model, 2026, [arXiv:cs.CR/2602.07422]

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.340607Z

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Observation 58b7f4f5-c3a5-45cf-be22-b23a823b2c1f · outbound

This paper cites R+ r: Security vulnerability dataset quality is critical.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python R+ r: Security vulnerability dataset quality is critical

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.412314Z

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Observation c8393cf5-a5cc-4e1e-a27e-62198326a545 · outbound

This paper cites Analyzing source code vulnerabilities in the D2A dataset with ML ensembles and C-BERT.Empirical Softw.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Analyzing source code vulnerabilities in the D2A dataset with ML ensembles and C-BERT.Empirical Softw

Reference 29

Resolution: verified exact; doi, observed 2026-08-01T08:38:28.247819Z

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 93fa9042-0f37-46c3-963d-92e45d7ec874 · outbound

This paper cites Lora: Low-rank adaptation of large language models.Iclr2022,1, 3.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Lora: Low-rank adaptation of large language models.Iclr2022,1, 3

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.599242Z

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Observation 4ed4765f-c50e-48f7-bffb-9946813737db · outbound

This paper cites Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.695248Z

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Observation e86326bc-dc81-4e17-83f6-fcf33e6d5555 · outbound

This paper cites Unleashing Artificial Cognition: Integrating Multiple AI Systems.Australasian Conference on Information Systems2024.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Unleashing Artificial Cognition: Integrating Multiple AI Systems.Australasian Conference on Information Systems2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.754592Z

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Observation 1caeef22-fbb2-43a9-8398-072920ef6da8 · outbound

This paper cites Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.841873Z

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Observation ae3d95af-ede4-4851-a36d-2ca85a2990d6 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences2017,114, 3521–3526.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences2017,114, 3521–3526

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:07.951888Z

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Observation 9577a231-8004-4a62-9857-ee636f42fea1 · outbound

This paper cites an unresolved cited work.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:08.105645Z

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Observation b9e43df3-ae72-4f90-b86e-0129f8b58e24 · outbound

This paper cites On Information and Sufficiency.Annals of Mathematical Statistics1951, 22, 79–86.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python On Information and Sufficiency.Annals of Mathematical Statistics1951, 22, 79–86

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:08.265221Z

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Observation fb1dd7c8-bc09-4672-809f-f213041ebfde · outbound

This paper cites Adam: A method for stochastic optimization.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Adam: A method for stochastic optimization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:08.396559Z

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Observation f8792c30-059e-4d9d-8990-0ba46f8033da · outbound

This paper cites Qwen2.5-Coder Technical Report.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Qwen2.5-Coder Technical Report

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:08.566496Z

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Observation 5130eede-dcf4-47fe-8dea-a6ef7939cff8 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.Nat Mach Intell 5 2023, pp.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python Parameter-efficient fine-tuning of large-scale pre-trained language models.Nat Mach Intell 5 2023, pp

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:08.715134Z

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Observation 1b5c2035-e0be-498b-aa86-7bf01b5afb6c · outbound

This paper cites A Study of BFLOAT16 for Deep Learning Training.

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python A Study of BFLOAT16 for Deep Learning Training

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:08.870384Z

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Pith citing papers

No inbound Pith citation observations are available.