Paper Citation Record · LEDGER

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code

As of 10 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2502.02412.

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

pith.paper-citation-record.v1
2502.02412 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:18:49.114898Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T16:37:16.968173Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 6b5349eb-fcc6-40e2-8f7a-0aaf17ff5b12 · outbound

This paper cites Impacts of software and its engineering on the carbon footprint of ict,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Impacts of software and its engineering on the carbon footprint of ict,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.632931Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8dd9e4e7-a42a-4c55-8c71-93873d92d6a6 · outbound

This paper cites Is software “green.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Is software “green

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.619956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d44d3e4-5101-4314-83d1-13b9c31af001 · outbound

This paper cites An empirical study of practitioners’ perspectives on green software engineering,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code An empirical study of practitioners’ perspectives on green software engineering,

Reference 3

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Observation cf4352a7-d05e-4f38-a872-f6bf888ccad9 · outbound

This paper cites A comprehensive review of green computing: Past, present, and future research,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A comprehensive review of green computing: Past, present, and future research,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.581208Z

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Observation e3ca8ad8-4a19-4438-84c7-1ee7e14f98ba · outbound

This paper cites Future data center energy- conservation and emission-reduction technologies in the context of smart and low-carbon city construction,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Future data center energy- conservation and emission-reduction technologies in the context of smart and low-carbon city construction,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.511117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 282e7461-c2d0-4f99-b775-c0f471e8a33a · outbound

This paper cites Ict sector electricity consumption and greenhouse gas emissions – 2020 outcome,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Ict sector electricity consumption and greenhouse gas emissions – 2020 outcome,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.432532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c950a4c-1b43-410a-a8e0-c64fe1f03cc0 · outbound

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

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:48.982183Z

Source-reported events for the cited work

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Observation 4fc51840-9dab-403f-a842-10232263207a · outbound

This paper cites Green artificial intelligence initiatives: Potentials and challenges,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Green artificial intelligence initiatives: Potentials and challenges,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.366600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc11fcc3-6990-4433-b6d5-739d36b0a4a5 · outbound

This paper cites Ai-powered green cloud and data center,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Ai-powered green cloud and data center,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.353639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0cdfcdeb-d5b2-4646-a5cd-c3e458898872 · outbound

This paper cites Trends in ai inference energy consumption: Beyond the performance-vs- parameter laws of deep learning,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Trends in ai inference energy consumption: Beyond the performance-vs- parameter laws of deep learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.341829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f0bf52d-e505-4280-892b-cedbc0011273 · outbound

This paper cites Estimating the carbon footprint of bloom, a 176b parameter language model,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Estimating the carbon footprint of bloom, a 176b parameter language model,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.330508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 64856773-8fa5-46f3-b44b-cfeb6d7e39e5 · outbound

This paper cites Reducing carbon footprint in ai: A framework for sustainable training of large language models,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Reducing carbon footprint in ai: A framework for sustainable training of large language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.319669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7cad6f4e-5ced-4788-be24-86b9f2c45265 · outbound

This paper cites A review of green artificial intelligence: Towards a more sustainable future,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A review of green artificial intelligence: Towards a more sustainable future,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.307966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4cdfb38a-7e68-458f-9258-6f813b54639b · outbound

This paper cites Github repository: Energyefficiencyllmcode,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Github repository: Energyefficiencyllmcode,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.296377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9659cdd2-e317-4f71-85cd-c8c9e05bc0d4 · outbound

This paper cites A review on code generation with llms: Application and evaluation,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A review on code generation with llms: Application and evaluation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.284563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80895743-4774-461b-ad1a-f9b1edf80b84 · outbound

This paper cites Evaluating the quality of llm-generated explanations for logical errors in cs1 student programs,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating the quality of llm-generated explanations for logical errors in cs1 student programs,

Reference 16

Resolution: metadata mismatch; raw_fallback, observed 2026-08-09T12:18:49.993184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42b00ceb-9800-49ba-a75a-f10af7201def · outbound

This paper cites Evaluating Large Language Models Trained on Code.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating Large Language Models Trained on Code

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.016737Z

Source-reported events for the cited work

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Observation a3aef581-a744-49cc-97b1-031cd26a7fed · outbound

This paper cites On sample-efficient code generation,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code On sample-efficient code generation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.271342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51c2380b-61f2-41de-985b-77be395d0bbb · outbound

This paper cites Enhancing software development efficiency through ai- powered code generation,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Enhancing software development efficiency through ai- powered code generation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.258378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2d2e9bb3-44db-41ce-9c4c-87dbd5e221c1 · outbound

This paper cites Assessing the quality of github copilot’s code generation,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Assessing the quality of github copilot’s code generation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.028096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ddb12fb-11be-4ace-89f9-c91f45389866 · outbound

This paper cites A performance study of llm-generated code on leetcode,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A performance study of llm-generated code on leetcode,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.032367Z

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Observation 406d7856-c4f2-4b9f-b811-512d5fe80b32 · outbound

This paper cites Ranking programming languages by energy efficiency,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Ranking programming languages by energy efficiency,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.246776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 822e8bfc-dac8-478b-8a0a-30098a4d8b37 · outbound

This paper cites Mercury: A Code Efficiency Benchmark for Code Large Language Models.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Mercury: A Code Efficiency Benchmark for Code Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.040624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62024d86-28e7-46f3-9ac5-a342f004b447 · outbound

This paper cites EffiBench: Benchmarking the Efficiency of Automatically Generated Code.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code EffiBench: Benchmarking the Efficiency of Automatically Generated Code

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.045744Z

Source-reported events for the cited work

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Observation 76c84be8-4dc3-46b6-a075-3e9d0a5a4693 · outbound

This paper cites Codereval: A benchmark of pragmatic code generation with generative pre-trained models,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Codereval: A benchmark of pragmatic code generation with generative pre-trained models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.234342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5cc8448c-d6aa-491b-b7ed-bde54e8ab2ae · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Measuring Coding Challenge Competence With APPS

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.058003Z

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Observation c281ff54-b71e-4d38-be5c-65d4d82bb5d0 · outbound

This paper cites ReCode: Robustness evaluation of code generation models,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code ReCode: Robustness evaluation of code generation models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.154197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14adca8b-58d1-40e0-a177-07bc45b600ea · outbound

This paper cites EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.070518Z

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Observation 92af4e47-120b-4794-91bb-615aa660321b · outbound

This paper cites Evaluating Language Models for Efficient Code Generation.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating Language Models for Efficient Code Generation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.074682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce9636a0-b15d-4b97-84e8-4ec2de3a01c1 · outbound

This paper cites Evaluating large language models in class-level code generation,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating large language models in class-level code generation,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.078771Z

Source-reported events for the cited work

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Observation 2fe06cee-792d-4b49-a26d-47ed4a9fb628 · outbound

This paper cites Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.082383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16eaa6a8-91a3-43f4-b302-91d94f74ce87 · outbound

This paper cites An empirical evaluation of github copilot’s code suggestions,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code An empirical evaluation of github copilot’s code suggestions,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.129181Z

Source-reported events for the cited work

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Observation 868eb686-fbfc-496a-ae13-7996ddc30ab4 · outbound

This paper cites Generation probabilities are not enough: Uncertainty highlighting in ai code completions,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Generation probabilities are not enough: Uncertainty highlighting in ai code completions,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.090408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92f70fce-25fd-400b-ac3e-77b386fc8e10 · outbound

This paper cites van Solingen (Revision), V.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code van Solingen (Revision), V

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.094572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf811f66-ae3c-467c-be83-8cb3357c5dbd · outbound

This paper cites On evaluating the efficiency of source code generated by llms,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code On evaluating the efficiency of source code generated by llms,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.101298Z

Source-reported events for the cited work

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Observation e0202362-c378-4914-a9fa-30cdbaa9edde · outbound

This paper cites Intel® performance counter monitor - a better way to measure cpu.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Intel® performance counter monitor - a better way to measure cpu

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.114627Z

Source-reported events for the cited work

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Observation df1d2b21-9c38-4517-b091-27ff705fc316 · outbound

This paper cites RAPL in action: Experiences in using RAPL for power measurements,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code RAPL in action: Experiences in using RAPL for power measurements,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.102134Z

Source-reported events for the cited work

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Observation f4d3c422-657f-4e2a-876d-07db6fd25ae2 · outbound

This paper cites Understanding energy behaviors of thread management constructs,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Understanding energy behaviors of thread management constructs,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.111686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43f67bb2-82c5-4525-b644-7bd461d235ca · outbound

This paper cites Energy efficiency analysis of compiler optimizations on the spec cpu 2017 benchmark suite,.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Energy efficiency analysis of compiler optimizations on the spec cpu 2017 benchmark suite,

Reference 39

Resolution: metadata mismatch; raw_fallback, observed 2026-08-09T12:18:49.274293Z

Source-reported events for the cited work

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Observation ceff4b4d-f363-48e9-947d-99ceac883cdb · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 234790100.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Available: https://api.semanticscholar.org/CorpusID: 234790100

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:18:50.189476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb793cdf-3fd4-4bed-8b3a-fbdff12a2b5f · outbound

This paper cites Available: https://doi.org/10.1145/3597503.3623316.

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Available: https://doi.org/10.1145/3597503.3623316

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T12:18:49.054144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 96c31ea6-e5c9-49cc-90e1-b61f321409fc · inbound

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code cites this paper.

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:40:10.190034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.