Pith. sign in

Paper Citation Record · LEDGER

Routing LLM Inference to the Cleanest Grid in Real Time

As of 8 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2608.06188.

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

pith.paper-citation-record.v1
2608.06188 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:31.923370Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

31 of 31 outbound references displayed

  • verified exact6
  • verified fuzzy7
  • unresolved17
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 47448fd7-2e8b-49dc-a41a-a3291721df11 · outbound

This paper cites Marginal Emissions Factors for the U.S. Electricity System.

Routing LLM Inference to the Cleanest Grid in Real Time Marginal Emissions Factors for the U.S. Electricity System

Reference 1

Resolution
verified exact
doi, observed 2026-08-07T13:00:33.426997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:28.925594Z digest=sha256:58d238264ef35c283083bf9b5b56646fa6f12c52fd74c8a9796a9cf9e881fec5

Observation 1455f23e-b40d-4eda-b93d-0221111d4ee0 · outbound

This paper cites Marginal Operating Emissions Rate (MOER): methodology and validation.

Routing LLM Inference to the Cleanest Grid in Real Time Marginal Operating Emissions Rate (MOER): methodology and validation

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:00:36.039428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:28.979003Z digest=sha256:b0bab018003be36f78bcb6db86cbd4919b5e958c63099f9d352abdad486d775d

Observation e7bc1a16-3dda-438c-bfbb-1c271882c5e5 · outbound

This paper cites An Introduction to the Bootstrap.

Routing LLM Inference to the Cleanest Grid in Real Time An Introduction to the Bootstrap

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:00:35.789109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:29.080724Z digest=sha256:38d206291e1226bf7a6e827c6847dca539565305abea5e9da27578645b4d7919

Observation ae05443b-d64e-4ea9-a6e3-975851def28e · outbound

This paper cites Carbon-Aware Computing for Datacenters.

Routing LLM Inference to the Cleanest Grid in Real Time Carbon-Aware Computing for Datacenters

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.193102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.193102Z digest=sha256:68ef7026adb96f7067c0997c6f322c575a96a037ca57545fb72a721244553d42

Observation fffdd508-c9fa-485a-88b1-fb33357372b7 · outbound

This paper cites Let’ s Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud.

Routing LLM Inference to the Cleanest Grid in Real Time Let’ s Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.305345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.305345Z digest=sha256:fee12637e5c0de89cc722cb004fb37529bbfb897b22116d0cf9116e7d0ad7969

Observation e82bac1f-378a-4cf8-bdb2-2985f87b497d · outbound

This paper cites Chasing Carbon: The Elusive Environmental Footprint of Computing.

Routing LLM Inference to the Cleanest Grid in Real Time Chasing Carbon: The Elusive Environmental Footprint of Computing

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.468536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.468536Z digest=sha256:c8116224c0647a35858cbbfadbd4d6849cdcb0b4a51476bf2117408220498d3a

Observation 7ae5d640-01ff-46da-af9d-1ca92e4fd04e · outbound

This paper cites Ecovisor: A Virtual Energy System for Carbon-Efficient Applications.

Routing LLM Inference to the Cleanest Grid in Real Time Ecovisor: A Virtual Energy System for Carbon-Efficient Applications

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.582641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.582641Z digest=sha256:8389641d992860e233cc700d890f8fcdcd2957388e19d998c937c59dfc0b48be

Observation c90662ff-b400-457c-9fe5-dcf41e3a837c · outbound

This paper cites CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency.

Routing LLM Inference to the Cleanest Grid in Real Time CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.681481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.681481Z digest=sha256:cbbfdbaa15d89c190650672f5d1383ebdb77fe6d3fea6703c0dea707cdb87d84

Observation 39bf7065-21dc-4ee0-97d6-e9ea9fc99b2b · outbound

This paper cites Greening Geographical Load Balancing.

Routing LLM Inference to the Cleanest Grid in Real Time Greening Geographical Load Balancing

Reference 10

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T13:00:34.011615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:29.766712Z digest=sha256:7c3b1281d19a2d8a3e226fd1536cb331b364dcbd825603d357492e5836197352

Observation 2ac576d9-9d38-478a-be79-073ac1e8aa63 · outbound

This paper cites Real-Time Carbon Accounting Method for the European Electricity Markets.

Routing LLM Inference to the Cleanest Grid in Real Time Real-Time Carbon Accounting Method for the European Electricity Markets

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.838041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.838041Z digest=sha256:ce197726e8eaf43adcb4ea73756150b94ad09e1c4e1d7ed90bfdb55d894d55c7

Observation 63255441-7677-48d0-8005-a96fe3940d50 · outbound

This paper cites Power Hungry Processing: Watts Driving the Cost of AI Deployment?.

Routing LLM Inference to the Cleanest Grid in Real Time Power Hungry Processing: Watts Driving the Cost of AI Deployment?

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.921396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.921396Z digest=sha256:2d48ed3d7c52a4480e91826ff7b46fdae5831cceab802576064d883188c5add3

Observation e03550cb-6947-442a-8e01-021fd2261017 · outbound

This paper cites Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service.

Routing LLM Inference to the Cleanest Grid in Real Time Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:29.999001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:29.999001Z digest=sha256:57288eb6d0cbbc19b18f1f62b306316f6bcf2114f39ab523d61cbbe6561459d2

Observation ba88a26e-6969-4f5f-b3e4-98aae1bed28a · outbound

This paper cites Sprout: Green Generative AI with Carbon-Efficient LLM Inference.

Routing LLM Inference to the Cleanest Grid in Real Time Sprout: Green Generative AI with Carbon-Efficient LLM Inference

Reference 14

Resolution
verified exact
doi, observed 2026-08-07T13:00:33.183484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:30.112490Z digest=sha256:4988a08721eec0200b4b3ad58ef3b7abd93de80b3f9b1be27f04692b893ca021

Observation d3fa7796-cfc8-47ce-9837-7dde5963efa4 · outbound

This paper cites DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency.

Routing LLM Inference to the Cleanest Grid in Real Time DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency

Reference 15

Resolution
verified exact
doi, observed 2026-08-07T13:00:32.984354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:30.239349Z digest=sha256:317879e4a3454016a0eed2ebf027a960d58b677044f75e7ce8f70fcac39a477c

Observation 69785945-e45f-46f1-aa25-0a6aafd38773 · outbound

This paper cites Towards Environmentally Equitable AI via Geographical Load Balancing.

Routing LLM Inference to the Cleanest Grid in Real Time Towards Environmentally Equitable AI via Geographical Load Balancing

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:30.318532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:30.318532Z digest=sha256:d2973621a1c65cac20936e7f22820d79693cc6c76fbcdca31c6bc8838f11e95f

Observation 1a303a26-20db-44d3-a460-9ee8d61c5bee · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

Routing LLM Inference to the Cleanest Grid in Real Time Energy and Policy Considerations for Deep Learning in NLP

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:30.407480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:30.407480Z digest=sha256:8b1292a8a0df6e218172ff6a35a248d9c030815d1a94a346da7eb67b76aeae03

Observation 732dad1d-95e2-4600-bbf0-62194ce8a950 · outbound

This paper cites Measuring the Carbon Intensity of AI in Cloud Instances.

Routing LLM Inference to the Cleanest Grid in Real Time Measuring the Carbon Intensity of AI in Cloud Instances

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:30.488926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:30.488926Z digest=sha256:2638dea4192d6b60db0204025931bb5640f0bdb3a9b9f4605e0725f71deb9863

Observation a9b505ca-63f0-44c3-bb9f-ebe2f343ea95 · outbound

This paper cites Estimating Marginal CO2 Emissions Rates for National Electricity Systems.

Routing LLM Inference to the Cleanest Grid in Real Time Estimating Marginal CO2 Emissions Rates for National Electricity Systems

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:30.590547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:30.590547Z digest=sha256:03599c320ca0f24c2cbd40700f93479981514e471e8795a620ac0e0625c42daa

Observation 11e2b943-2064-4579-8160-5e08f3ad9e45 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Routing LLM Inference to the Cleanest Grid in Real Time Carbon Emissions and Large Neural Network Training

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:30.630891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:30.630891Z digest=sha256:5c7a3c358fbf38f4e232d9b8e2f30378dc1e2d6fa296039a7911e869509ed186

Observation 5646786f-c0a6-4687-a2ff-6597f600aa71 · outbound

This paper cites TowardstheSystematic Reporting of the Energy and Carbon Footprints of Machine Learning.

Routing LLM Inference to the Cleanest Grid in Real Time TowardstheSystematic Reporting of the Energy and Carbon Footprints of Machine Learning

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:00:35.580513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:30.788875Z digest=sha256:dc130d0744771e9b4e5832031e4e6db1a8c9fb4539032acc508c0850d3dc7474

Observation 43a98908-a110-409e-81be-d3c40d812166 · outbound

This paper cites Solyx AI Grid: Hardware-Telemetry-Aware Routing Across Geographically Distributed GPU Clusters.

Routing LLM Inference to the Cleanest Grid in Real Time Solyx AI Grid: Hardware-Telemetry-Aware Routing Across Geographically Distributed GPU Clusters

Reference 22

Resolution
verified exact
doi, observed 2026-08-07T13:00:32.686524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:30.866693Z digest=sha256:145f14f549c8c8427600ed3bce30ba5fd0249412a9411203cf5bf947fd6c4610

Observation c58e9d3b-f534-4daf-afa9-1372a189abb5 · outbound

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

Routing LLM Inference to the Cleanest Grid in Real Time EcoServe: Designing Carbon-Aware AI Inference Systems

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:30.958563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:30.958563Z digest=sha256:c5eebadc025a4db4ebd6becc596e4db1f7011bb7b021ed6467bd22222367fe9e

Observation 13b15205-ebc9-44fe-a7c7-7cfa0a8827bf · outbound

This paper cites Serving Models, Fast and Slow: Optimizing Heterogeneous LLM Inferencing Workloads at Scale (SAGESERVE).

Routing LLM Inference to the Cleanest Grid in Real Time Serving Models, Fast and Slow: Optimizing Heterogeneous LLM Inferencing Workloads at Scale (SAGESERVE)

Reference 24

Resolution
verified exact
doi, observed 2026-08-07T13:00:32.454291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:31.199614Z digest=sha256:4bcf73a4af53318a79c6abff42da515dc476ceb3f176dea57f0860ab9d635344

Observation f3e6692b-2605-4406-9297-f2d904efb5c2 · outbound

This paper cites GAR: Carbon-Aware Routing for LLM Inference via Constrained Optimization.

Routing LLM Inference to the Cleanest Grid in Real Time GAR: Carbon-Aware Routing for LLM Inference via Constrained Optimization

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:00:32.162092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:31.324204Z digest=sha256:ce29fbd93c2343e1e84887778e4af6bd7a5fabb4e4600a27a9fa6f271e403bf4

Observation ca612e8b-fdd6-422c-8092-ca6911bde83d · outbound

This paper cites v3 API documentation.

Routing LLM Inference to the Cleanest Grid in Real Time v3 API documentation

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:00:35.324632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:31.475248Z digest=sha256:d21d9c3a1feecd7a4cbd7bbb01c815ca111b17039f7ae9875f66097a9db85390

Observation 06e93b2c-f960-4d59-baa6-e2e09638481c · outbound

This paper cites North-America MOER model version 2026-03-01 release notes (expanded renewable- curtailment detection; zero-MOER values during modeled curtailment).

Routing LLM Inference to the Cleanest Grid in Real Time North-America MOER model version 2026-03-01 release notes (expanded renewable- curtailment detection; zero-MOER values during modeled curtailment)

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:00:35.113190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:31.624372Z digest=sha256:2498de0cdc30234b7dc59bb226353705fa1f08319cefd099c04be5fa331a8725

Observation 3384a559-c0ff-4e10-8389-edf7e8ecd3ea · outbound

This paper cites The GHG Protocol for Project Accounting.

Routing LLM Inference to the Cleanest Grid in Real Time The GHG Protocol for Project Accounting

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:00:34.922297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:31.734682Z digest=sha256:48605844c626b5bb90740cb790efeb07eeb16247e5c3159203fba64d76357c73

Observation b291e807-2dd8-4536-9923-94fc48250d60 · outbound

This paper cites Guidelines for Quantifying GHG Reductions from Grid-Connected Electricity Projects.

Routing LLM Inference to the Cleanest Grid in Real Time Guidelines for Quantifying GHG Reductions from Grid-Connected Electricity Projects

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:00:34.716609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:31.827948Z digest=sha256:ee39da742761160e13ef0daeed292219e59eb99486d64d409d23f2511e863e2d

Observation 60a57892-9a93-4d95-967d-9c5d5f564666 · outbound

This paper cites an unresolved cited work.

Routing LLM Inference to the Cleanest Grid in Real Time Unresolved cited work

Reference 2007

Resolution
unresolved
raw_fallback, observed 2026-08-07T13:00:34.514625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:00:31.923370Z digest=sha256:d6ade21fcd6afd36360f3f6bd247d194fe8a15e84f0e0c1d32e3d478f606ea43

Observation 2c19ea45-c4fc-442c-a38d-19d9aca21f10 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Routing LLM Inference to the Cleanest Grid in Real Time Carbon Emissions and Large Neural Network Training

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:30.717634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:30.717634Z digest=sha256:6282c228a7ce01aa9058a6cf5baca48a50f67a86ddb689b671d4bb1609ac335e

Observation bb7ba158-1ba9-450c-8a7b-44929659aa1d · outbound

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

Routing LLM Inference to the Cleanest Grid in Real Time EcoServe: Designing Carbon-Aware AI Inference Systems

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-07T13:00:31.049555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:31.049555Z digest=sha256:d8c62d72d2dd469ed787b2795e1d44ecc26d640a7e68d6fa7989cc68ad26b61c

Pith citing papers

No inbound Pith citation observations are available.