{"as_of":"2026-08-08T07:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e73035a19c0fdc754c510c9791701466179ce4efccbcdea60922ea46484f0946","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:10:37.192524Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.08727/citation-record","integrity":"/paper/2506.08727/integrity","json":"/paper/2506.08727/citation-record.json","paper":"/paper/2506.08727"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.418605Z","title":"Reducing the carbon impact of generative ai inference (today and in 2035),","venue":null,"work_id":"f35d295c-65bc-43f7-8cbe-e4e8f0a1eaae","year":2023},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.132698Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:a891205d167ab91e98ae2b0184c18a20d6abba899f7227c1c7b455c100810f91","observation_id":"53f41991-257d-4c67-8e1d-ef3d81570ca6","resolution":{"observed_at":"2026-08-07T05:10:37.422878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.404697Z","title":null,"venue":null,"work_id":"70e05b6c-a79e-474a-96a2-fefcbc07cfe9","year":2024},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.136875Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:e65890f239c276d818c1cd95b8e16f840b8c5f8e909c9b585b0a04039d8b2635","observation_id":"d1ddb359-f5a5-4882-8ea6-47bb2614b265","resolution":{"observed_at":"2026-08-07T05:10:37.408681Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.140403Z","title":"The carbon footprint of machine learning training will plateau, then shrink,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.140403Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:a9b1478ba61eb2e0c3ac41528132c80bbfa6e4777b9b9eae42a23904bab394d2","observation_id":"e54ca34a-39a8-4df8-8de2-8c67849a63f9","resolution":{"observed_at":"2026-08-07T05:10:37.140403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.144040Z","title":"Green ai,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.144040Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:5a81eff24ba4088cf4c758bac1b31ebda8a90ffde7232c1f71127724b5ccb15a","observation_id":"ee458821-b201-4fff-889f-6a59718eef9b","resolution":{"observed_at":"2026-08-07T05:10:37.144040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.373135Z","title":null,"venue":null,"work_id":"bada34e8-857b-42cc-b3f0-ab9927d34002","year":2024},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.147715Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:ea2f64b6aa17e1b3c0030063a33f838f4cc58b1d0ba9ec049690fcfa5acf1102","observation_id":"fc35c1dd-419e-4dc3-926f-d5c47ed775aa","resolution":{"observed_at":"2026-08-07T05:10:37.377846Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.151647Z","title":"mlco2/codecarbon: v2.4.1,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.151647Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:755eaa4948677b216d0fa83ee91829e79a64d5584cc430dba64e5677e3c5ecf7","observation_id":"de4e9497-c2f6-4c67-a365-bbf318fc8d43","resolution":{"observed_at":"2026-08-07T05:10:37.151647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.360253Z","title":"Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai,","venue":null,"work_id":"47b6e58c-e4f1-48bf-a93a-4fb6956ff3d0","year":2022},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.155567Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:80cec35f0b6a384ada6240f49249225cb5eee5bfdde9f316d26c10d0fc1b1032","observation_id":"b618018d-fa4c-4e88-a942-245ae908b6d1","resolution":{"observed_at":"2026-08-07T05:10:37.364624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.344444Z","title":"LLMCarbon: Modeling the end-to-end carbon footprint of large language models,","venue":null,"work_id":"c2482582-469b-4668-a748-f3a18587c3bb","year":2024},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.159183Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:7dd962eff45ffc3695f4937ede61e05e6a7dd9c01690e8758dcf755c1a656aa8","observation_id":"7cddd638-3295-473a-ab93-92418adb3325","resolution":{"observed_at":"2026-08-07T05:10:37.350332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.09700","last_updated":"2019-11-04T20:37:33Z","snapshot_observed_at":"2026-07-06T08:31:12.309726Z","submitted_at":"2019-10-21T23:57:32Z","title":"Quantifying the Carbon Emissions of Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.09700","snapshot_observed_at":"2026-08-07T05:10:37.162642Z","title":"Quanti- fying the carbon emissions of machine learning,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.162642Z"},"links":{"cited_paper":"/paper/1910.09700","citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:41071e063c8e825be1e355b3e77266e7eadd2ac04d66de3485b1dc3156fa5803","observation_id":"47958575-efb2-4990-bc6c-7c6afadfff1a","resolution":{"observed_at":"2026-08-07T05:10:37.162642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.329979Z","title":null,"venue":null,"work_id":"3dec3bd0-1b45-4a2d-aefd-aff19b37c63e","year":2024},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.166515Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:6bff2c66b1efb6b8a9e6edc0a198da13eeceb7da4d2e06a92cb64d1ee990c36f","observation_id":"aa3c0fdf-a8fb-4726-9de2-21eafc68c102","resolution":{"observed_at":"2026-08-07T05:10:37.334157Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09110","last_updated":"2023-10-01T21:44:23Z","snapshot_observed_at":"2026-08-01T19:14:56.803459Z","submitted_at":"2022-11-16T18:51:34Z","title":"Holistic Evaluation of Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.09110","snapshot_observed_at":"2026-08-07T05:10:37.169704Z","title":"Holistic evaluation of language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.169704Z"},"links":{"cited_paper":"/paper/2211.09110","citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:66b91497182165861d7aed1734cbd481f308eafb127b8c2439a5281311851bba","observation_id":"0e50d866-5b5a-462c-ba6a-267b6524b1a1","resolution":{"observed_at":"2026-08-07T05:10:37.169704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.314305Z","title":"Cheaply estimating inference efficiency metrics for autoregressive transformer models,","venue":null,"work_id":"0ee26aad-f873-492a-a80e-c7febc52d3ec","year":2023},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.173872Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:5e1eb834da42c79c3f95eed1d386233554c721c5b56a3fe76fa1af4e1d0e2a0b","observation_id":"87447d01-3c09-4e49-841c-d4a93580e8ce","resolution":{"observed_at":"2026-08-07T05:10:37.320084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.300153Z","title":"Beyond efficiency: Scaling ai sustainably,","venue":null,"work_id":"76777dc0-82d7-4e30-9aa7-2c593578d0c0","year":2024},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.177705Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:39d02f8f4edbe95b8a91b84fe720783c681bcee55ffbdc04e6f2294266ca15f1","observation_id":"ee0d0ea8-9987-4839-8ae4-f993fc0f91af","resolution":{"observed_at":"2026-08-07T05:10:37.304191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.284314Z","title":"Estimating the carbon footprint of bloom, a 176b parameter language model,","venue":null,"work_id":"a8dbaf2f-bee2-4f02-8453-e3c7752e9037","year":2023},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.181053Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:b4fcbb76cb9a119ca1d468c8e42d290d031ff2db6030cf5d8f6606846a8f471b","observation_id":"0274c939-7f20-4c9e-a220-58ac03150652","resolution":{"observed_at":"2026-08-07T05:10:37.290384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.268632Z","title":"Llm-perf leaderboard,","venue":null,"work_id":"7968168d-cac4-4fc3-ae5c-346e6543e10a","year":2023},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.184481Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:8fe6d882731a67c54edbf9760fcea76dd133b208d6dca34a6474870f47f5bd68","observation_id":"3df75510-af65-470f-b809-e5eb06e6343a","resolution":{"observed_at":"2026-08-07T05:10:37.274300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.17323","last_updated":"2023-03-22T13:10:47Z","snapshot_observed_at":"2026-08-07T08:38:54.025062Z","submitted_at":"2022-10-31T13:42:40Z","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.17323","snapshot_observed_at":"2026-08-07T05:10:37.188192Z","title":"Gptq: Accurate post-training quantization for generative pre-trained transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.188192Z"},"links":{"cited_paper":"/paper/2210.17323","citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:cc680e753aaf4ab2bac7de14727faa0c996c675f8de5a168a6ea8592b7363795","observation_id":"41de91d2-5739-4a0f-a38d-39c9994745fb","resolution":{"observed_at":"2026-08-07T05:10:37.188192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:10:37.192524Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:37.192524Z"},"links":{"citing_paper":"/paper/2506.08727"},"observation_digest":"sha256:b472efaecc7d18e45cd5530bc548c6493ca933fe43555de43b4fb7f00fc9374c","observation_id":"f383da64-6c76-4344-b567-6cc6fecd87ae","resolution":{"observed_at":"2026-08-07T05:10:37.192524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.08727","last_updated":"2025-06-10T12:23:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T05:01:04.259313Z","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":17},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"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."}