Paper Citation Record · LEDGER

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

As of 21 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 4 inbound Pith citation observations for arXiv:2508.08332.

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

pith.paper-citation-record.v1
2508.08332 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:13:44.136049Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T17:00:48.664985Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T18:50:16.704191Z

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy12
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1879b8c5-c6d1-4699-890a-bc7bbf352fd3 · outbound

This paper cites Herrington, Code generation in action.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Herrington, Code generation in action

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:48.549074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0be26535-fed6-496b-8aff-8f0372d9c9f1 · outbound

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

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Trends in ai inference energy consumption: Beyond the performance-vs-parameter laws of deep learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:48.196724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99ad9f69-f0ac-4e83-bdb7-7aab6e2602c6 · outbound

This paper cites Holistically Evaluating the Environmental Impact of Creating Language Models.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Holistically Evaluating the Environmental Impact of Creating Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:40.278171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fcfbcda-a5f0-47a8-a145-6c1129be27a3 · outbound

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

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:40.473741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8a85329-b15f-4c62-a21a-6b10ef185844 · outbound

This paper cites What is the Role of Small Models in the LLM Era: A Survey.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming What is the Role of Small Models in the LLM Era: A Survey

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:40.643363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b65bc2d-dbd8-4b21-8bc6-f73b448f6880 · outbound

This paper cites Mercury: A code efficiency benchmark for code large language models,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Mercury: A code efficiency benchmark for code large language models,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:47.837951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d8839770-2201-4f56-a1ba-ff8270b74bd7 · outbound

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

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Effibench: Benchmarking the efficiency of automatically generated code,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:47.407777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 798f480c-c09f-4e77-b4be-6e52a9a18d36 · outbound

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

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Codereval: A benchmark of pragmatic code generation with generative pre-trained models,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:41.204810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41f0dffb-f6ba-40a5-8a1c-a728239cef02 · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Measuring Coding Challenge Competence With APPS

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:41.322409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cce6614-0f5c-45eb-9d0e-a93f0d189c8d · outbound

This paper cites ReCode: Robustness Evaluation of Code Generation Models.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming ReCode: Robustness Evaluation of Code Generation Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:41.517789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95ec1eda-cb95-44d4-9d16-34ff0b603877 · outbound

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

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:41.656485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97eb19f5-cacd-4a67-8ab4-081d3a46b654 · outbound

This paper cites An exploration of prompting llms to generate energy-efficient code,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming An exploration of prompting llms to generate energy-efficient code,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:47.120808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9297f77e-5d72-4f9f-824e-d5fabd20fa8c · outbound

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

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:41.984742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c039f3d-3ffe-4a5b-b461-617afaacb39e · outbound

This paper cites Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:42.186801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8a20653-720b-443e-9949-9ae704fde7d8 · outbound

This paper cites Comparative analysis of carbon footprint in manual vs. llm-assisted code development,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Comparative analysis of carbon footprint in manual vs. llm-assisted code development,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:46.759630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1e69139d-dd0d-4f12-be1d-3cfa1a0d5805 · outbound

This paper cites Green My LLM: Studying the key factors affecting the energy consumption of code assistants.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Green My LLM: Studying the key factors affecting the energy consumption of code assistants

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:42.553964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57a8a41b-bbde-4ea8-b4d6-f3544a7a8cab · outbound

This paper cites Sustainability via LLM Right-sizing.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Sustainability via LLM Right-sizing

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:13:44.484629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

This paper cites Evaluating the Energy-Efficiency of the Code Generated by LLMs.

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

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:42.933664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7c7e9ef-186e-4d41-bb3f-ac24538a7019 · outbound

This paper cites Can llms generate green code-a comprehensive study through leetcode,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Can llms generate green code-a comprehensive study through leetcode,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:46.454596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b9c594fd-0857-4071-9a4f-39211b52c28d · outbound

This paper cites A controlled experiment on the energy efficiency of the source code generated by code llama,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming A controlled experiment on the energy efficiency of the source code generated by code llama,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:46.184348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb0bb92f-2fc3-4a4d-8b01-d2da6116c80e · outbound

This paper cites How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T22:13:43.518709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01814f8e-d18e-4756-9064-c73661aada68 · outbound

This paper cites Carbon footprint evaluation of code generation through llm as a service,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Carbon footprint evaluation of code generation through llm as a service,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:45.903815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff33927f-2926-45ac-bb2d-1cc4c4854a29 · outbound

This paper cites Ai-powered, but power- hungry? energy efficiency of llm-generated code,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming Ai-powered, but power- hungry? energy efficiency of llm-generated code,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:45.649110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72e26b7f-9bc4-4ece-858d-2c5cf58a3e86 · outbound

This paper cites The goal question metric approach,.

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming The goal question metric approach,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:45.296116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f00909c3-856d-473c-ae9a-b4f0c1c9961e · outbound

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

Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming On evaluating the efficiency of source code generated by llms,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:13:44.907131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 36782193-2377-4100-87b3-d0a0b5bc78d5 · 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 Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.707415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64c50ad2-8ed8-4cca-bc9d-8bc9aa8f99c9 · inbound

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation cites this paper.

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:13:13.221058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 10257a53-8e6c-4dab-ac02-00a74de8274a · inbound

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

EcoAssist: Embedding Sustainability into AI-Assisted Frontend Development Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T21:55:48.982382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 40850954-7663-4e02-af7f-3c6ebd7ef88b · inbound

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning cites this paper.

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-11T17:00:48.664985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.