Pith. sign in

Paper Citation Record · LEDGER

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs

As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.08727.

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

pith.paper-citation-record.v1
2506.08727 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:10:37.192524Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 53f41991-257d-4c67-8e1d-ef3d81570ca6 · outbound

This paper cites Reducing the carbon impact of generative ai inference (today and in 2035),.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Reducing the carbon impact of generative ai inference (today and in 2035),

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:10:37.422878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.132698Z digest=sha256:a891205d167ab91e98ae2b0184c18a20d6abba899f7227c1c7b455c100810f91

Observation d1ddb359-f5a5-4882-8ea6-47bb2614b265 · outbound

This paper cites an unresolved cited work.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:10:37.408681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.136875Z digest=sha256:e65890f239c276d818c1cd95b8e16f840b8c5f8e909c9b585b0a04039d8b2635

Observation e54ca34a-39a8-4df8-8de2-8c67849a63f9 · outbound

This paper cites The carbon footprint of machine learning training will plateau, then shrink,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs The carbon footprint of machine learning training will plateau, then shrink,

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:37.140403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:37.140403Z digest=sha256:a9b1478ba61eb2e0c3ac41528132c80bbfa6e4777b9b9eae42a23904bab394d2

Observation ee458821-b201-4fff-889f-6a59718eef9b · outbound

This paper cites Green ai,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Green ai,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:37.144040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:37.144040Z digest=sha256:5a81eff24ba4088cf4c758bac1b31ebda8a90ffde7232c1f71127724b5ccb15a

Observation fc35c1dd-419e-4dc3-926f-d5c47ed775aa · outbound

This paper cites an unresolved cited work.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:10:37.377846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.147715Z digest=sha256:ea2f64b6aa17e1b3c0030063a33f838f4cc58b1d0ba9ec049690fcfa5acf1102

Observation de4e9497-c2f6-4c67-a365-bbf318fc8d43 · outbound

This paper cites mlco2/codecarbon: v2.4.1,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs mlco2/codecarbon: v2.4.1,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:37.151647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:37.151647Z digest=sha256:755eaa4948677b216d0fa83ee91829e79a64d5584cc430dba64e5677e3c5ecf7

Observation b618018d-fa4c-4e88-a942-245ae908b6d1 · outbound

This paper cites Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:10:37.364624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.155567Z digest=sha256:80cec35f0b6a384ada6240f49249225cb5eee5bfdde9f316d26c10d0fc1b1032

Observation 7cddd638-3295-473a-ab93-92418adb3325 · outbound

This paper cites LLMCarbon: Modeling the end-to-end carbon footprint of large language models,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs LLMCarbon: Modeling the end-to-end carbon footprint of large language models,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:10:37.350332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.159183Z digest=sha256:7dd962eff45ffc3695f4937ede61e05e6a7dd9c01690e8758dcf755c1a656aa8

Observation 47958575-efb2-4990-bc6c-7c6afadfff1a · outbound

This paper cites Quantifying the Carbon Emissions of Machine Learning.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Quantifying the Carbon Emissions of Machine Learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:37.162642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:37.162642Z digest=sha256:41071e063c8e825be1e355b3e77266e7eadd2ac04d66de3485b1dc3156fa5803

Observation aa3c0fdf-a8fb-4726-9de2-21eafc68c102 · outbound

This paper cites an unresolved cited work.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:10:37.334157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.166515Z digest=sha256:6bff2c66b1efb6b8a9e6edc0a198da13eeceb7da4d2e06a92cb64d1ee990c36f

Observation 0e50d866-5b5a-462c-ba6a-267b6524b1a1 · outbound

This paper cites Holistic Evaluation of Language Models.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Holistic Evaluation of Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:37.169704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:37.169704Z digest=sha256:66b91497182165861d7aed1734cbd481f308eafb127b8c2439a5281311851bba

Observation 87447d01-3c09-4e49-841c-d4a93580e8ce · outbound

This paper cites Cheaply estimating inference efficiency metrics for autoregressive transformer models,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Cheaply estimating inference efficiency metrics for autoregressive transformer models,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:10:37.320084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.173872Z digest=sha256:5e1eb834da42c79c3f95eed1d386233554c721c5b56a3fe76fa1af4e1d0e2a0b

Observation ee0d0ea8-9987-4839-8ae4-f993fc0f91af · outbound

This paper cites Beyond efficiency: Scaling ai sustainably,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Beyond efficiency: Scaling ai sustainably,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:10:37.304191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.177705Z digest=sha256:39d02f8f4edbe95b8a91b84fe720783c681bcee55ffbdc04e6f2294266ca15f1

Observation 0274c939-7f20-4c9e-a220-58ac03150652 · outbound

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

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Estimating the carbon footprint of bloom, a 176b parameter language model,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:10:37.290384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.181053Z digest=sha256:b4fcbb76cb9a119ca1d468c8e42d290d031ff2db6030cf5d8f6606846a8f471b

Observation 3df75510-af65-470f-b809-e5eb06e6343a · outbound

This paper cites Llm-perf leaderboard,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Llm-perf leaderboard,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:10:37.274300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T05:10:37.184481Z digest=sha256:8fe6d882731a67c54edbf9760fcea76dd133b208d6dca34a6474870f47f5bd68

Observation 41de91d2-5739-4a0f-a38d-39c9994745fb · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:37.188192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:37.188192Z digest=sha256:cc680e753aaf4ab2bac7de14727faa0c996c675f8de5a168a6ea8592b7363795

Observation f383da64-6c76-4344-b567-6cc6fecd87ae · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness,.

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs Flashattention: Fast and memory-efficient exact attention with io-awareness,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:37.192524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:37.192524Z digest=sha256:b472efaecc7d18e45cd5530bc548c6493ca933fe43555de43b4fb7f00fc9374c

Pith citing papers

No inbound Pith citation observations are available.