Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 3 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 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:53.190047Z

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

Source-reported events for the cited work

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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

Source-reported events for the cited work

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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

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

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.708530Z

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 2026-08-10T06:31:04.303077+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

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

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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Source-reported events for the cited work

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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

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:49:26.513808Z

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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

Source-reported events for the cited work

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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

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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

Source-reported events for the cited work

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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 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 2026-08-10T06:31:04.303077+00:00.