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Paper Citation Record · LEDGER

LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2309.14393.

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

pith.paper-citation-record.v1
2309.14393 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:31:35.646240Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:05:46.397672Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f2242299-863c-4b8f-b325-9094d10aaffa · inbound

EcoServe: Designing Carbon-Aware AI Inference Systems cites this paper.

EcoServe: Designing Carbon-Aware AI Inference Systems LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-08T20:31:35.646240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:31:35.646240Z digest=sha256:5f7cfb5baab250bd075472a10614c1d40b220daf8f83dee80efe8cbd5fc299ff

Observation f3648170-c39c-42d9-83d0-d7e1bd67d9d6 · inbound

Beyond Vision: How Large Language Models Interpret Facial Expressions from Valence-Arousal Values cites this paper.

Beyond Vision: How Large Language Models Interpret Facial Expressions from Valence-Arousal Values LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-08T19:06:48.569516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:06:48.569516Z digest=sha256:b456ef6d9f55a117aa72b919271f0a1fd80dea999327c18a8fc7308fad66a0e4

Observation 56481f6f-420f-4eaf-92e6-e5b9f9095cea · inbound

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference cites this paper.

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 28

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verified exact
arxiv_id, observed 2026-05-23T00:07:17.290521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T00:05:26.205947Z digest=sha256:1a1d285e17e4aab7382bd1295d7f94f11d24a4ec5c483dd9918153953355a273

Observation 54d9b4db-9fb7-4734-9ae9-fff583816ab7 · inbound

Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time cites this paper.

Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T12:44:19.930035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:44:19.930035Z digest=sha256:5e41f6e9add9d5e32be81dfc70d75b3299dfef3ee53668061374af53fb613908

Observation bfaf3d68-9e6d-41b0-9a53-a672eee2a5ab · inbound

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights cites this paper.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T11:18:58.457714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:18:58.457714Z digest=sha256:0990e291fe38488f961c3bda37def187294e8f4e67e4b29b7bf1fe865294fed1

Observation 10e97ba5-576b-4db9-a67b-8cf8d15ea5e3 · inbound

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead cites this paper.

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T04:47:06.691656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:47:06.691656Z digest=sha256:f1ceada54866ff860dcb568fdee339b4a92a933b82ff2dee4f26eb5b0a1e3c62

Observation ac81000c-57ba-4351-80f4-f706671c9d8e · inbound

Analysis of Propaganda in Tweets From Politically Biased Sources cites this paper.

Analysis of Propaganda in Tweets From Politically Biased Sources LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 5646

Resolution
unresolved
no resolver link, observed 2026-08-06T18:29:56.991783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:29:56.991783Z digest=sha256:53a66bf9dbacb9d3ef5025a7940194f54395cf91ed8163941f6800cd3b22b965

Observation 3dc804c7-5911-4df3-b883-304ed3fc2922 · inbound

CEO-DC: Driving Decarbonization in HPC Data Centers with Actionable Insights cites this paper.

CEO-DC: Driving Decarbonization in HPC Data Centers with Actionable Insights LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T18:18:48.139078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:18:48.139078Z digest=sha256:b58521b1dfe3e8803f8e6c4696c3ccb5a668ff446b880e6ae1e9311cac21d69c

Observation a1ca63f3-9cf2-48ad-9a5f-db29b2da5bc2 · inbound

Performance is not All You Need: Sustainability Considerations for Algorithms cites this paper.

Performance is not All You Need: Sustainability Considerations for Algorithms LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T17:03:25.827775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:03:25.827775Z digest=sha256:17c9cf739fe302fbad1b12b5c4239d1cb991d1cfb51334baedf6dff298759a41

Observation c6820ef8-acea-4a44-a257-838dfffb1b0a · inbound

Throttling Web Agents Using Reasoning Gates cites this paper.

Throttling Web Agents Using Reasoning Gates LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T12:28:03.894314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:28:03.894314Z digest=sha256:be2aa4901aa3051c6e3ea5d522b5048f5edafa477817843b3d396cbc90c9c79c

Observation 73339394-2495-4107-8c13-703eba13a886 · inbound

EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization cites this paper.

EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:48:33.269930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 16c3cf6f-1063-43c1-9ef1-420e9bcfee41 · inbound

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:09:07.316240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T22:07:34.292536Z digest=sha256:0fd079a6cc03aafda6e5887b8e9cd445b74f8ea65c1b8d0203fa2d32deea4d7c

Observation 068ef811-52b6-40fc-b4a9-fa0082f8014f · inbound

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-01T14:05:46.399550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T22:21:16.608148Z digest=sha256:4a4e21844686e5e26aa9e67b21b919dba4a524b917babd35685ce4b201fb41e5