{"as_of":"2026-08-11T09:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aaeccf974167490e2b490a353e4705a0750d916309041352898400c57701b5dd","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":"2026-08-07T01:05:46.188015Z","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":"2026-08-11T06:34:44.6726+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/2506.12000/citation-record","integrity":"/paper/2506.12000/integrity","json":"/paper/2506.12000/citation-record.json","paper":"/paper/2506.12000"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1984.10960","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:05:47.545008Z","title":"Data Compression Using Adaptive Coding and Partial String Matching,","venue":null,"work_id":"4ff6560c-4327-4729-9a3d-da86c9d09c82","year":1984},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:44.869274Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:c4ca1740d338b656e214cbd4ac4b509b8ced94369b4861b33dd4b934a3a96f18","observation_id":"035c1a2d-a2c1-4aa6-8912-974a0eb29c44","resolution":{"observed_at":"2026-08-07T01:05:47.687591Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-07T01:05:48.538038Z","title":"Knoll, CMIX, 2014","venue":null,"work_id":"166be91c-550c-48e8-b22a-37268eac9b3e","year":2014},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:44.906782Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:b611469bf2dbb54f9b7ad66d740bb07e9bd17a73cf0764ed45c98c7c88a0e896","observation_id":"0d334266-a9a4-4441-98fa-f93c7bdd4386","resolution":{"observed_at":"2026-08-07T01:05:48.543481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-07T01:05:48.516235Z","title":"Knoll, Tensorflow -compress, 2016","venue":null,"work_id":"6d814c2b-4658-4505-88ad-ed68d01d349b","year":2016},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.008731Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:19d2ed194495b14b1257614680700deaa7b04a193d2faecf1a0b03f5ea1e972f","observation_id":"c5e2f084-4c22-4a25-9d34-321e75c725fe","resolution":{"observed_at":"2026-08-07T01:05:48.523997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-07T01:05:45.097078Z","title":"DZip: improved general-purpose loss less compression based on novel neural network modeling,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.097078Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:860df517ea1beb848761388293f7ef544d86569fe0bfe70f18110daa03961298","observation_id":"63cf1ef1-0885-4f33-a6c2-a1c29bbfdc14","resolution":{"observed_at":"2026-08-07T01:05:45.097078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.16114","last_updated":"2022-04-01T14:41:36Z","snapshot_observed_at":"2026-07-06T12:54:39.660679Z","submitted_at":"2022-03-30T07:46:19Z","title":"A Fast Transformer-based General-Purpose Lossless Compressor","version":2},"cited_work":{"arxiv_id":"2203.16114","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.16114","snapshot_observed_at":"2026-08-07T01:05:47.181479Z","title":"A Fast Transformer-based General-Purpose Lossless Compressor","venue":"cs.LG","work_id":"1cb51429-efcf-4d98-a6d0-7c729d782cf6","year":2022},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.173909Z"},"links":{"cited_paper":"/paper/2203.16114","citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:a3e1c274a1996ab135ddcbbced8fe12aebbe88109286796724dc2951cced354d","observation_id":"2565a4dd-6bc2-449f-bd1b-b7c218bad9c0","resolution":{"observed_at":"2026-08-07T01:05:47.219772Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.13003","last_updated":"2020-09-28T01:20:15Z","snapshot_observed_at":"2026-07-06T09:59:13.502662Z","submitted_at":"2020-09-28T01:20:15Z","title":"On Efficient Constructions of Checkpoints","version":1},"cited_work":{"arxiv_id":"2009.13003","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.13003","snapshot_observed_at":"2026-08-07T01:05:46.955466Z","title":"On Efficient Constructions of Checkpoints","venue":"cs.LG","work_id":"23ef1ffc-a2b3-4647-b4be-f32b628aff9d","year":2020},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.237882Z"},"links":{"cited_paper":"/paper/2009.13003","citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:4c151483fc679a88ed377cfc4c61d83201aae6775be72667987e6509b19520e6","observation_id":"cd7c71aa-5fda-4aed-ab06-db8a4c82fb58","resolution":{"observed_at":"2026-08-07T01:05:47.087015Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-07T01:05:48.456593Z","title":"Delta-dnn: efficiently compressing deep neural networks via exploiting floats similarity","venue":null,"work_id":"985b4183-89f6-4014-90d3-ac99510366eb","year":2020},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.326136Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:d00bdfa00d2b79cda08e0a8c22c818643cb982997786bef860fe6df0a374905e","observation_id":"58f198ea-885a-4f45-b86d-868ee23b987e","resolution":{"observed_at":"2026-08-07T01:05:48.502290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.32308","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T01:05:46.751663Z","title":"Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning,","venue":null,"work_id":"a63e98da-634c-43e4-b898-45748bd0b3c3","year":2023},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.371427Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:3ad9a16765b99197e38597ba2fa521e2afebc2ce2bc242875278a2f7edb70f28","observation_id":"9cd5dd9d-e71f-477b-86b4-4dbd5310882d","resolution":{"observed_at":"2026-08-07T01:05:46.853492Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11800","last_updated":"2024-10-12T03:51:25Z","snapshot_observed_at":"2026-08-04T19:29:20.354498Z","submitted_at":"2023-06-20T18:00:31Z","title":"Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization","version":3},"cited_work":{"arxiv_id":"2306.11800","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.11800","snapshot_observed_at":"2026-08-07T01:05:46.449103Z","title":"Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization","venue":"cs.LG","work_id":"5024d7d9-4275-43fc-8148-d5f2128c4931","year":2023},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.491644Z"},"links":{"cited_paper":"/paper/2306.11800","citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:b1cb3104e196516a65b284810b40055df2119ba172abbf393a2fb9982ba2000e","observation_id":"13a21908-c12f-44ae-a53e-702b44c57092","resolution":{"observed_at":"2026-08-07T01:05:46.534529Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11257","last_updated":"2024-06-17T06:47:29Z","snapshot_observed_at":"2026-08-11T03:41:54.038559Z","submitted_at":"2024-06-17T06:47:29Z","title":"ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11257","snapshot_observed_at":"2026-08-07T01:05:45.570929Z","title":"ExCP: Extreme LLM Checkpoint Compression via Weight -Momentum Joint Shrinking","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.570929Z"},"links":{"cited_paper":"/paper/2406.11257","citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:872fc68f500a2757723f7713cdf52f7192f4b7461f9d0480e627ae64a993125a","observation_id":"f52bc883-1b97-421e-bc4f-0cf90db7760e","resolution":{"observed_at":"2026-08-07T01:05:45.570929Z","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":"2026-08-07T01:05:48.240365Z","title":"Long short-term memory","venue":null,"work_id":"1f68e760-de8a-462f-8a03-bb424dab2cb3","year":1997},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.646172Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:a4e5e9481a89bc62914cfd3ea75d10d3dabb338ea0d2f11a380338614414892c","observation_id":"b47cae0a-6b44-4840-8832-0b875c595d2d","resolution":{"observed_at":"2026-08-07T01:05:48.349067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-07T01:05:48.007167Z","title":"Arithmetic coding for data compression,","venue":null,"work_id":"1c5cb532-8709-4fcd-b469-9c101f1c5947","year":1987},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.842955Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:e7874e7d2edb5807a4bbc4485ac55a38d8f808e5f62844a81771dd32fff85364","observation_id":"59e21a81-0246-431f-8a3b-60bdd864365b","resolution":{"observed_at":"2026-08-07T01:05:48.093715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-07T01:05:45.951921Z","title":"An image is worth 16x16 words: transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.951921Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:7d758263fafdfea819d5bb1f62b6f62dd06e84d5fda5f591cb71104ce8c30e08","observation_id":"7efe319f-f031-469e-b799-3efc6ef05a7b","resolution":{"observed_at":"2026-08-07T01:05:45.951921Z","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":"2026-08-07T01:05:47.817145Z","title":"Pythia: a suite for analyzing large language models across training and scaling","venue":null,"work_id":"7f05baae-f363-443a-8b4d-6d7c6922c02f","year":2023},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:46.042156Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:fcd7aa29647f6096ec0a1722718bae135bc2304ce0ea0f8e0d7c4b42a19806cd","observation_id":"df32d14d-39b1-4731-b4ce-9ca23dd319cc","resolution":{"observed_at":"2026-08-07T01:05:47.894618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.00027","last_updated":"2020-12-31T19:00:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-12-31T19:00:10Z","title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00027","snapshot_observed_at":"2026-08-07T01:05:46.126601Z","title":"The pile: an 800gb dataset of diverse text for language modeling","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:46.126601Z"},"links":{"cited_paper":"/paper/2101.00027","citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:32757edebf871611ecabde6a4c480d0a0480f4a134eda6a5506ae9ab0e5b0b65","observation_id":"cac6f51e-300a-4c84-938d-1ddf2af009e6","resolution":{"observed_at":"2026-08-07T01:05:46.126601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-07T01:05:46.188015Z","title":"Adam: a method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:46.188015Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:c8fed6d9ce622498d096589d20254cce6b6dc9b1f4800996e94889679430db64","observation_id":"682c3060-8b80-4d22-a302-12e60e8ee896","resolution":{"observed_at":"2026-08-07T01:05:46.188015Z","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":"2026-08-07T01:05:45.742568Z","title":null,"venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:45.742568Z"},"links":{"citing_paper":"/paper/2506.12000"},"observation_digest":"sha256:244d86802d28ae419f5c30b78cb6dcb2fab9eb45a06ac51930e533a03f5effb6","observation_id":"180d7a64-c4f4-4b80-8cbb-6e8a41569c1d","resolution":{"observed_at":"2026-08-07T01:05:45.742568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.12000","last_updated":"2025-06-13T17:54:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T00:56:09.753494Z","submitted_at":"2025-06-13T17:54:42Z","title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":6,"verified_exact":3,"verified_fuzzy":6},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.12000."}