{"as_of":"2026-08-23T17:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cc83ed8c3a23869df488753df25f9214810f7e37f53bad70b406732d7ee974ba","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:05:48.877154Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T20:05:49.415073Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.02697","last_updated":"2023-06-05T08:38:25Z","snapshot_observed_at":"2026-08-16T15:26:37.525689Z","submitted_at":"2023-06-05T08:38:25Z","title":"Efficient GPT Model Pre-training using Tensor Train Matrix Representation","version":1},"cited_work":{"arxiv_id":"2306.02697","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.02697","snapshot_observed_at":"2026-08-15T20:05:49.415073Z","title":"Efficient GPT Model Pre-training using Tensor Train Matrix Representation","venue":"cs.AI","work_id":"552f7017-80d8-42bd-84e5-8bb25a15f25f","year":2023},"citing_paper":{"arxiv_id":"2506.13514","last_updated":"2025-06-16T14:09:43Z","snapshot_observed_at":"2026-08-17T11:46:56.501732Z","submitted_at":"2025-06-16T14:09:43Z","title":"TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T20:05:48.877154Z"},"links":{"cited_paper":"/paper/2306.02697","citing_paper":"/paper/2506.13514"},"observation_digest":"sha256:2f429c92896628883e9a2994bfe0d4dfd7a8923f69e6f4c8853b0e71a9b8c6cf","observation_id":"fe1dc146-262c-4fed-9aba-89f156fbb013","resolution":{"observed_at":"2026-08-15T20:05:49.421397Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.02697/citation-record","integrity":"/paper/2306.02697/integrity","json":"/paper/2306.02697/citation-record.json","paper":"/paper/2306.02697"},"outbound":[],"paper":{"arxiv_id":"2306.02697","last_updated":"2023-06-05T08:38:25Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T15:26:37.525689Z","submitted_at":"2023-06-05T08:38:25Z","title":"Efficient GPT Model Pre-training using Tensor Train Matrix Representation"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2306.02697."}