Paper Citation Record · LEDGER

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation

As of 12 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 4 inbound Pith citation observations for arXiv:2501.11006.

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

pith.paper-citation-record.v1
2501.11006 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:49:26.249674Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:30:06.928550Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T22:59:34.252595Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation def6f0de-65ed-48b9-a638-4bcca224cba3 · outbound

This paper cites Github copilot,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Github copilot,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.906141Z

Source-reported events for the cited work

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Observation fabad406-5681-4c70-9b4a-6573c368f3e3 · outbound

This paper cites Amazon q developer, ai for software development.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Amazon q developer, ai for software development

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.889303Z

Source-reported events for the cited work

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Observation 1dc07455-905c-4b2a-b296-e9c92a3c3f40 · outbound

This paper cites Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 3

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Source-reported events for the cited work

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Observation 009896c9-71e7-4602-a3ca-5da22b74d4de · outbound

This paper cites The growing energy footprint of artificial intelligence,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation The growing energy footprint of artificial intelligence,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T18:49:26.055352Z

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Observation 768f8897-4dee-4e9d-ba3b-8c7b6eb7470a · outbound

This paper cites Qlora: efficient finetuning of quantized llms,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Qlora: efficient finetuning of quantized llms,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.861205Z

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Observation 8ba70289-a75a-49ed-8703-e2776ac7442b · outbound

This paper cites Quip: 2-bit quanti- zation of large language models with guarantees,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Quip: 2-bit quanti- zation of large language models with guarantees,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.845338Z

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Observation 5f0d3f58-468d-4219-877f-6faf3b5f052b · outbound

This paper cites Fp8 quantization: the power of the exponent,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Fp8 quantization: the power of the exponent,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.828025Z

Source-reported events for the cited work

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Observation dfb58da2-3f78-4965-8f49-baa731508d69 · outbound

This paper cites Post-training quantization with multiple points: Mixed precision without mixed precision,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Post-training quantization with multiple points: Mixed precision without mixed precision,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.811877Z

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Observation 5e175729-bbae-40da-a32a-4f0fa5c25552 · outbound

This paper cites TinyBERT: Distilling BERT for natural language understanding,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation TinyBERT: Distilling BERT for natural language understanding,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.794742Z

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Observation 9047bede-434f-4180-92a6-2d31d7b81e14 · outbound

This paper cites Mixed distillation helps smaller language models reason better,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Mixed distillation helps smaller language models reason better,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.768496Z

Source-reported events for the cited work

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Observation 410cc8e8-d9a4-4331-8be7-78bdbd64e95e · outbound

This paper cites Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty

Reference 11

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Observation 90d9b99f-39d9-475d-b8fc-38537a4f76e6 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Llm-pruner: On the structural pruning of large language models,

Reference 12

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Observation 8a06c929-e860-421b-a595-3f93b3a94814 · outbound

This paper cites LaCo: Large language model pruning via layer collapse,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation LaCo: Large language model pruning via layer collapse,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.742155Z

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Observation 788c029d-5fc7-4062-b4eb-b6dad4dafe56 · outbound

This paper cites Structured optimal brain pruning for large language models,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Structured optimal brain pruning for large language models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.725206Z

Source-reported events for the cited work

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Observation dac9e415-2ee9-4fa1-b6d0-c151c81052fa · outbound

This paper cites DeeBERT: Dynamic early exiting for accelerating BERT inference,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation DeeBERT: Dynamic early exiting for accelerating BERT inference,

Reference 15

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Source-reported events for the cited work

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Observation d67e5611-683b-4512-bf8d-555ef8a0b0f1 · outbound

This paper cites Berxit: Early exiting for bert with better fine-tuning and extension to regression,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Berxit: Early exiting for bert with better fine-tuning and extension to regression,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.691405Z

Source-reported events for the cited work

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Observation 3fc30af4-dbe3-4366-9768-14f99f6f310d · outbound

This paper cites Confident adaptive language modeling,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Confident adaptive language modeling,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.672319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 090f15bd-4140-44e1-8232-b06d0456dd15 · outbound

This paper cites When neural code completion models size up the situation: Attaining cheaper and faster completion through dynamic model inference,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation When neural code completion models size up the situation: Attaining cheaper and faster completion through dynamic model inference,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.654108Z

Source-reported events for the cited work

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Observation 582706ad-5932-4de1-8f1f-252d4d90506a · outbound

This paper cites Consistentee: A consistent and hardness-guided early exiting method for accelerating language models inference,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Consistentee: A consistent and hardness-guided early exiting method for accelerating language models inference,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.637665Z

Source-reported events for the cited work

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Observation 38804f84-bdbc-490e-b01e-55f3fc1e120e · outbound

This paper cites A data-driven frequency scaling approach for deadline-aware energy efficient schedul- ing on graphics processing units (gpus),.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation A data-driven frequency scaling approach for deadline-aware energy efficient schedul- ing on graphics processing units (gpus),

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.620688Z

Source-reported events for the cited work

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Observation 8c005a4e-d4b9-45f1-9f27-206c7c18f5db · outbound

This paper cites Accelerating llama inference by enabling intermediate layer decoding via instruction tuning with lite,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Accelerating llama inference by enabling intermediate layer decoding via instruction tuning with lite,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.603052Z

Source-reported events for the cited work

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Observation ec50f2ce-f880-45fe-a756-61f267d9b5eb · outbound

This paper cites Codexglue: A machine learning benchmark dataset for code understanding and generation,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Codexglue: A machine learning benchmark dataset for code understanding and generation,

Reference 22

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Observation b17100df-bfa2-428f-904c-0a3317db24ae · outbound

This paper cites Mining source code repositories at mas- sive scale using language modeling,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Mining source code repositories at mas- sive scale using language modeling,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.574625Z

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Observation c0a24f6f-6b70-4631-891b-1bf7efc7b044 · outbound

This paper cites Probabilistic model for code with decision trees,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Probabilistic model for code with decision trees,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.556448Z

Source-reported events for the cited work

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Observation 123168e6-14b8-4474-9da2-436bc95e9afc · outbound

This paper cites Opt: Open pre-trained transformer language models,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Opt: Open pre-trained transformer language models,

Reference 25

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Observation de5a8bb6-9683-4176-9c76-a8da49e56ebd · outbound

This paper cites Llama: Open and efficient foundation language models,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Llama: Open and efficient foundation language models,

Reference 26

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Observation a187a681-5a20-4a4a-974d-006cd1bb2514 · outbound

This paper cites Attention is all you need,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Attention is all you need,

Reference 27

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Observation 2c7bb21a-2d01-4b60-8ca2-a1955c604506 · outbound

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GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Unresolved cited work

Reference 28

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Observation 4a5dd9b5-ae01-40e6-a605-e4d8c829cc0c · outbound

This paper cites Gymnasium: A standard interface for reinforcement learning environments,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Gymnasium: A standard interface for reinforcement learning environments,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.481873Z

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Observation a2cb20be-fa69-478f-9e0d-7dadcfe5d916 · outbound

This paper cites Openai gym,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Openai gym,

Reference 30

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Observation 6370ffd7-4911-4a6c-9125-ea06379acabc · outbound

This paper cites Stable-baselines3: reliable reinforcement learning implementa- tions,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Stable-baselines3: reliable reinforcement learning implementa- tions,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.453082Z

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Observation 0633dbfa-2ea4-4d2c-9c3d-37c1ef330f50 · outbound

This paper cites Proximal policy optimization algorithms,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Proximal policy optimization algorithms,

Reference 32

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Source-reported events for the cited work

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Observation b577a26c-7478-405c-aef0-87391e08edb5 · outbound

This paper cites Out of the bleu: How should we assess quality of the code generation models?.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Out of the bleu: How should we assess quality of the code generation models?

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.423870Z

Source-reported events for the cited work

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Observation 9b64fc40-9c0c-41b3-aab6-b496f5fd9069 · outbound

This paper cites Codebleu: a method for automatic evaluation of code synthesis,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Codebleu: a method for automatic evaluation of code synthesis,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.404935Z

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Observation 03a40997-6fd4-45fc-ad8e-6ac41af2a4a4 · outbound

This paper cites LayerSkip: Enabling early exit inference and self-speculative decoding,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation LayerSkip: Enabling early exit inference and self-speculative decoding,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.387653Z

Source-reported events for the cited work

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Observation c1dbe6e3-5cb6-47f9-ac24-58845b124116 · outbound

This paper cites Investigating ac- celeration of LLaMA inference by enabling intermediate layer decoding via instruction tuning with ‘LITE’,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Investigating ac- celeration of LLaMA inference by enabling intermediate layer decoding via instruction tuning with ‘LITE’,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.368882Z

Source-reported events for the cited work

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Observation ac678142-a77a-47cd-a85d-6496f10cf62c · outbound

This paper cites Jump to conclusions: Short-cutting transformers with linear transformations,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Jump to conclusions: Short-cutting transformers with linear transformations,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.350238Z

Source-reported events for the cited work

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Observation ce4c23f1-a5f3-4b81-af57-d5c2c2b7638b · outbound

This paper cites A simple hash-based early exiting approach for language understanding and generation,.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation A simple hash-based early exiting approach for language understanding and generation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.331316Z

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Observation 52355e35-abe2-4edc-924c-45b2c8e59296 · outbound

This paper cites an unresolved cited work.

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation Unresolved cited work

Reference 2020

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Pith citing papers

Observation bd4a1094-6215-477c-a499-762afa8f4735 · inbound

Insights into resource utilization of code small language models serving with runtime engines and execution providers cites this paper.

Insights into resource utilization of code small language models serving with runtime engines and execution providers GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation

Reference 60

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

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

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

Reference 26

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Observation 4f4bbba3-b8aa-49ef-8ef1-d38992903544 · inbound

Babbling Suppression: Making LLMs Greener One Token at a Time cites this paper.

Babbling Suppression: Making LLMs Greener One Token at a Time GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:30:53.206569Z

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Observation 8e6434b5-3369-4390-9eb1-8ead580fe52a · inbound

Two-dimensional early exit optimisation of LLM inference cites this paper.

Two-dimensional early exit optimisation of LLM inference GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:59:34.276709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.