Paper Citation Record · LEDGER

Evaluating the Energy-Efficiency of the Code Generated by LLMs

As of 18 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 5 inbound Pith citation observations for arXiv:2505.20324.

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

pith.paper-citation-record.v1
2505.20324 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:54.506118Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:13:42.933664Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T05:26:02.110020Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy26
unresolved14
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 732b2bae-3c20-4f77-8dea-773d953afa85 · outbound

This paper cites Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 1

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Source-reported events for the cited work

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Observation b7379694-b917-4136-9c1a-701ad372409a · outbound

This paper cites Electricity consumption by ict: Facts, trends, and measurements.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Electricity consumption by ict: Facts, trends, and measurements

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:59.577765Z

Source-reported events for the cited work

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Observation caf3a952-6440-4c12-b513-e078c50f7448 · outbound

This paper cites Ai’s growing carbon footprint, June 2023.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Ai’s growing carbon footprint, June 2023

Reference 3

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Observation cf28b268-b9fd-4deb-8abb-823151856d0a · outbound

This paper cites Facts & figures, 2023.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Facts & figures, 2023

Reference 4

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Observation 0505bd51-0238-4b7b-bb40-03db3f353e69 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Carbon Emissions and Large Neural Network Training

Reference 5

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Source-reported events for the cited work

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Observation 1ce64d08-91e4-4168-b7dc-2901187fea5a · outbound

This paper cites Assessing ict global emissions footprint: Trends to 2040 & recommen- dations.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Assessing ict global emissions footprint: Trends to 2040 & recommen- dations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:59.180120Z

Source-reported events for the cited work

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Observation e00cfcb0-c60b-44c7-8d70-d2321e1ff515 · outbound

This paper cites Green ai: Energy-efficient training and inference of deep neural networks.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Green ai: Energy-efficient training and inference of deep neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:59.043629Z

Source-reported events for the cited work

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Observation b0aed11e-c545-48c4-adfd-c69e98cc1311 · outbound

This paper cites Effibench: Benchmarking the efficiency of automatically generated code.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Effibench: Benchmarking the efficiency of automatically generated code

Reference 8

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Observation ad0dd610-5141-481c-985a-083760f0464c · outbound

This paper cites Green software engineering: Metrics, practices, and tools.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Green software engineering: Metrics, practices, and tools

Reference 9

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Source-reported events for the cited work

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Observation a4f8c8a6-4e89-4e69-8612-990317870272 · outbound

This paper cites Green software engineering: The science of sustainable software development.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Green software engineering: The science of sustainable software development

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:58.656370Z

Source-reported events for the cited work

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Observation bf867524-d76d-41da-ba7f-d218da72ac37 · outbound

This paper cites Green measurement metrics towards a sustainable software: A systematic literature review.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Green measurement metrics towards a sustainable software: A systematic literature review

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:58.538621Z

Source-reported events for the cited work

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Observation 91974669-6a97-4ab5-9135-a823521eb507 · outbound

This paper cites An Energy-Aware Programming Approach for Mobile Application Development Guided by a Fine-Grained Energy Model.

Evaluating the Energy-Efficiency of the Code Generated by LLMs An Energy-Aware Programming Approach for Mobile Application Development Guided by a Fine-Grained Energy Model

Reference 12

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Observation 06a8db86-514b-48bd-9deb-dd54ef099fcb · outbound

This paper cites Energy efficiency in sustainable software development: Clean code approaches.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Energy efficiency in sustainable software development: Clean code approaches

Reference 13

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Source-reported events for the cited work

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Observation 6cec9963-5679-4da2-a169-b9cc474973f9 · outbound

This paper cites Advancing green comput- ing: Practices, strategies, and impact in modern software development for environmental sustainability.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Advancing green comput- ing: Practices, strategies, and impact in modern software development for environmental sustainability

Reference 14

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Source-reported events for the cited work

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Observation f309cb9e-6c66-47a7-9599-32d27e24ff8f · outbound

This paper cites Sustainable software engineering: Green coding’s impact on climate change.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Sustainable software engineering: Green coding’s impact on climate change

Reference 15

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Source-reported events for the cited work

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Observation 16033f1a-fd56-42bd-8ecd-4e2837f0bfa0 · outbound

This paper cites Towards the systematic reporting of the energy and carbon footprints of machine learning.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Towards the systematic reporting of the energy and carbon footprints of machine learning

Reference 16

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Source-reported events for the cited work

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Observation a7557d38-1c9d-4e0c-bae4-9c081a2d40a4 · outbound

This paper cites A holistic approach to environmentally sustainable computing.

Evaluating the Energy-Efficiency of the Code Generated by LLMs A holistic approach to environmentally sustainable computing

Reference 17

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Source-reported events for the cited work

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Observation 39b8d916-b349-46d9-af1d-e6458232bfd8 · outbound

This paper cites Green algorithms: Quantifying the carbon footprint of computation.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Green algorithms: Quantifying the carbon footprint of computation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:57.814715Z

Source-reported events for the cited work

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Observation d3e4d66b-94bd-45ea-b47c-c90e296d0e79 · outbound

This paper cites Enamel: Efficency automatic evaluator.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Enamel: Efficency automatic evaluator

Reference 19

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Source-reported events for the cited work

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Observation 45506f2f-a3e2-468d-80c7-ecbdc7e2e1ca · outbound

This paper cites Neural code intelligence: A new era in software engineering.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Neural code intelligence: A new era in software engineering

Reference 20

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Source-reported events for the cited work

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Observation 5c99f92a-d108-497e-b5d2-3943ee6e5c84 · outbound

This paper cites Functional correctness assessment of ai-generated code: Challenges and opportunities.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Functional correctness assessment of ai-generated code: Challenges and opportunities

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:57.335102Z

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Observation 7a760c77-c5cc-4f81-9b77-d6a46eefc512 · outbound

This paper cites On Evaluating the Efficiency of Source Code Generated by LLMs.

Evaluating the Energy-Efficiency of the Code Generated by LLMs On Evaluating the Efficiency of Source Code Generated by LLMs

Reference 22

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Source-reported events for the cited work

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Observation 2acf0eea-12f7-4581-9045-7b834a40de33 · outbound

This paper cites A Taxonomy of Inefficiencies in LLM-Generated Python Code.

Evaluating the Energy-Efficiency of the Code Generated by LLMs A Taxonomy of Inefficiencies in LLM-Generated Python Code

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:52.996905Z

Source-reported events for the cited work

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Observation 388d6a4c-4033-4e60-8afa-868100372ff2 · outbound

This paper cites Energy-aware prompt engineering for efficient code generation.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Energy-aware prompt engineering for efficient code generation

Reference 24

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Observation 3356c24b-b06c-49e9-b896-e226e0be3a90 · outbound

This paper cites Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions

Reference 25

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Observation 03922d37-abee-4347-b7d3-1e0f4e7a4ac3 · outbound

This paper cites GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation.

Evaluating the Energy-Efficiency of the Code Generated by LLMs GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:53.190047Z

Source-reported events for the cited work

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Observation 70535082-9e95-43d4-b802-7caefaa84441 · outbound

This paper cites Reinforcement learning with energy consumption feedback for efficient code generation.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Reinforcement learning with energy consumption feedback for efficient code generation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:56.997414Z

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Observation 18366b86-a0c2-43be-9a19-677582dc3760 · outbound

This paper cites Generating Energy-efficient code with LLMs.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Generating Energy-efficient code with LLMs

Reference 28

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Observation 69004e29-2dcd-4850-9a9b-429cf1c9dec5 · outbound

This paper cites Problems, 2025.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Problems, 2025

Reference 29

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Observation f8910ba3-0a92-49c5-9239-f20c0628169a · outbound

This paper cites perf-stat— Linux manual page , 2023.

Evaluating the Energy-Efficiency of the Code Generated by LLMs perf-stat— Linux manual page , 2023

Reference 30

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Observation a67f1d66-ca46-4527-b3be-30419e755ccc · outbound

This paper cites Gunawi, Cody Hammock, Joe Mambretti, Alexander Barnes, François Halbach, Alex Rocha, and Joe Stubbs.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Gunawi, Cody Hammock, Joe Mambretti, Alexander Barnes, François Halbach, Alex Rocha, and Joe Stubbs

Reference 31

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Observation a80bcfb5-d627-4b60-a559-7e74fc59c116 · outbound

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Evaluating the Energy-Efficiency of the Code Generated by LLMs Unresolved cited work

Reference 32

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Observation 18570b20-df7d-4995-923a-51fc66ea839f · outbound

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Evaluating the Energy-Efficiency of the Code Generated by LLMs Unresolved cited work

Reference 33

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Evaluating the Energy-Efficiency of the Code Generated by LLMs Unresolved cited work

Reference 34

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Observation 854edb93-3855-4507-b63d-96c13cfd1bb8 · outbound

This paper cites However, it does apply the pruning strategy to remove redundant calculation of symmetric paths.

Evaluating the Energy-Efficiency of the Code Generated by LLMs However, it does apply the pruning strategy to remove redundant calculation of symmetric paths

Reference 35

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Evaluating the Energy-Efficiency of the Code Generated by LLMs Unresolved cited work

Reference 36

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Observation 90b93564-f4b6-471c-9699-dbe342d93599 · outbound

This paper cites Although the logic produced by both LLMs is very similar, LLaMA 3.3 70B does more redundant calculations by evaluating the branches with the same values.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Although the logic produced by both LLMs is very similar, LLaMA 3.3 70B does more redundant calculations by evaluating the branches with the same values

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.815509Z

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Evaluating the Energy-Efficiency of the Code Generated by LLMs Unresolved cited work

Reference 38

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Observation 8fea3005-6921-4686-9154-296f823639e1 · outbound

This paper cites These models use an optimal approach using a few variables to iteratively compute the result.

Evaluating the Energy-Efficiency of the Code Generated by LLMs These models use an optimal approach using a few variables to iteratively compute the result

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.564819Z

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Observation 24669e04-d7d2-4088-978b-fce90645b958 · outbound

This paper cites This implementation relies on constructing a list with a size proportional to the maximum value in the input array, resulting in a time complexity of O(max(nums)).

Evaluating the Energy-Efficiency of the Code Generated by LLMs This implementation relies on constructing a list with a size proportional to the maximum value in the input array, resulting in a time complexity of O(max(nums))

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.435970Z

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Observation 3b56f93a-a0fe-4ad3-87f5-23097213394a · outbound

This paper cites Similar to the canonical approach, it uses a list-based method to accumulate points.

Evaluating the Energy-Efficiency of the Code Generated by LLMs Similar to the canonical approach, it uses a list-based method to accumulate points

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:37:55.279686Z

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Observation ff6b4d3b-a935-4a76-af3b-f31b37158d0a · outbound

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Evaluating the Energy-Efficiency of the Code Generated by LLMs Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:37:55.100223Z

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Pith citing papers

Observation 65074277-dac7-463d-b0a4-90d54e9ebebe · inbound

SysLLMatic: Large Language Models are Software System Optimizers cites this paper.

SysLLMatic: Large Language Models are Software System Optimizers Evaluating the Energy-Efficiency of the Code Generated by LLMs

Reference 20

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Observation 2ba7c30d-b853-4be0-8b33-acc5994aed68 · inbound

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming cites this paper.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Evaluating the Energy-Efficiency of the Code Generated by LLMs

Reference 18

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Observation 81086354-8792-49a2-8ba9-a11f5563e2c9 · inbound

Sustainable Code Generation Using Large Language Models: A Systematic Literature Review cites this paper.

Sustainable Code Generation Using Large Language Models: A Systematic Literature Review Evaluating the Energy-Efficiency of the Code Generated by LLMs

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.660366Z

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Observation 32888328-b966-412e-b19d-a227db276c08 · inbound

EcoAssist: Embedding Sustainability into AI-Assisted Frontend Development cites this paper.

EcoAssist: Embedding Sustainability into AI-Assisted Frontend Development Evaluating the Energy-Efficiency of the Code Generated by LLMs

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-10T21:55:48.902805Z

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Observation f7af266f-9773-414c-a8ba-29c095fc574d · inbound

Rethinking Code Performance Benchmarks for LLMs cites this paper.

Rethinking Code Performance Benchmarks for LLMs Evaluating the Energy-Efficiency of the Code Generated by LLMs

Reference 136

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T05:26:02.111759Z

Source-reported events for the cited work

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