{"as_of":"2026-08-20T03:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2c324f7bd1fc29c7585ae8ab5342616f7e094bef317ac767cc7b9262305a020d","coverage":[{"denominator":1,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T05:58:30.074525Z","state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T21:05:49.893508Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T00:39:16.838598Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.00806","last_updated":"2025-08-08T09:49:52Z","snapshot_observed_at":"2026-08-17T03:45:25.892940Z","submitted_at":"2025-08-01T17:39:25Z","title":"Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training","version":2},"cited_work":{"arxiv_id":"2508.00806","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.00806","snapshot_observed_at":"2026-07-04T00:39:16.838598Z","title":"arXiv preprint arXiv:2508.00806 , year=","venue":null,"work_id":"f03277f9-90c9-4c63-ab23-f87d6162a389","year":null},"citing_paper":{"arxiv_id":"2605.00539","last_updated":"2026-05-11T15:02:38Z","snapshot_observed_at":"2026-08-08T14:53:50.203528Z","submitted_at":"2026-05-01T09:39:03Z","title":"AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs","version":1},"reference_index":112,"source":"arxiv_source","source_observed_at":"2026-05-09T19:46:13.015064Z"},"links":{"cited_paper":"/paper/2508.00806","citing_paper":"/paper/2605.00539"},"observation_digest":"sha256:336da2c0151a52929ee6dd571a594fb4a349b710cc456326f31047656ae97a17","observation_id":"7fa41a23-20e3-4402-a2a5-fd279fa84e71","resolution":{"observed_at":"2026-05-11T15:31:18.086018Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.00806","last_updated":"2025-08-08T09:49:52Z","snapshot_observed_at":"2026-08-17T03:45:25.892940Z","submitted_at":"2025-08-01T17:39:25Z","title":"Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training","version":2},"cited_work":{"arxiv_id":"2508.00806","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.00806","snapshot_observed_at":"2026-07-04T00:39:16.838598Z","title":"arXiv preprint arXiv:2508.00806 , year=","venue":null,"work_id":"f03277f9-90c9-4c63-ab23-f87d6162a389","year":null},"citing_paper":{"arxiv_id":"2605.00539","last_updated":"2026-05-11T15:02:38Z","snapshot_observed_at":"2026-08-08T14:53:50.203528Z","submitted_at":"2026-05-01T09:39:03Z","title":"AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs","version":2},"reference_index":112,"source":"arxiv_source","source_observed_at":"2026-05-12T05:17:09.793360Z"},"links":{"cited_paper":"/paper/2508.00806","citing_paper":"/paper/2605.00539"},"observation_digest":"sha256:242ff3b3c8f4bdd6633ecf358ac55eaec1f7437700b87a891f6bd47b5201396b","observation_id":"3b1c5ac1-7aa0-4980-a4e4-460cf57f65ab","resolution":{"observed_at":"2026-05-12T05:21:30.749431Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.00806","last_updated":"2025-08-08T09:49:52Z","snapshot_observed_at":"2026-08-17T03:45:25.892940Z","submitted_at":"2025-08-01T17:39:25Z","title":"Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training","version":2},"cited_work":{"arxiv_id":"2508.00806","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.00806","snapshot_observed_at":"2026-07-04T00:39:16.838598Z","title":"arXiv preprint arXiv:2508.00806 , year=","venue":null,"work_id":"f03277f9-90c9-4c63-ab23-f87d6162a389","year":null},"citing_paper":{"arxiv_id":"2606.19528","last_updated":"2026-06-17T19:20:06Z","snapshot_observed_at":"2026-08-13T07:57:05.393300Z","submitted_at":"2026-06-17T19:20:06Z","title":"Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T21:05:49.893508Z"},"links":{"cited_paper":"/paper/2508.00806","citing_paper":"/paper/2606.19528"},"observation_digest":"sha256:a3b22026037378dea690c25761f573b770cc2652e3d05668fc0f3d00a8733490","observation_id":"6b46fc0a-7641-4618-9ba6-ffa5a09b0306","resolution":{"observed_at":"2026-07-04T00:39:16.840253Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.00806/citation-record","integrity":"/paper/2508.00806/integrity","json":"/paper/2508.00806/citation-record.json","paper":"/paper/2508.00806"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.00812","last_updated":"2025-08-25T16:10:31Z","snapshot_observed_at":"2026-08-12T15:15:06.938173Z","submitted_at":"2025-08-01T17:45:59Z","title":"On the controllability of the Kuramoto-Sivashinsky equation on multi-dimensional cylindrical domains","version":2},"cited_work":{"arxiv_id":"2508.00812","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.00812","snapshot_observed_at":"2026-08-06T05:58:30.186212Z","title":"On the controllability of the Kuramoto-Sivashinsky equation on multi-dimensional cylindrical domains","venue":"math.OC","work_id":"b84acb65-c97a-4511-8e15-88c6e1afc43e","year":2025},"citing_paper":{"arxiv_id":"2508.00806","last_updated":"2025-08-08T09:49:52Z","snapshot_observed_at":"2026-08-17T03:45:25.892940Z","submitted_at":"2025-08-01T17:39:25Z","title":"Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T05:58:30.074525Z"},"links":{"cited_paper":"/paper/2508.00812","citing_paper":"/paper/2508.00806"},"observation_digest":"sha256:5f8d5540d123eef5600e4e6e37397465ccf0f014005b9e09c96a34d15c30009c","observation_id":"47c06a09-c9ca-4eed-8635-678982d7ea5c","resolution":{"observed_at":"2026-08-06T05:58:30.280520Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.00806","last_updated":"2025-08-08T09:49:52Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T03:45:25.892940Z","submitted_at":"2025-08-01T17:39:25Z","title":"Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training"},"reference_resolution":{"displayed":1,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":1},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 1 of 1 outbound references and 3 inbound Pith citation observations for arXiv:2508.00806."}