{"as_of":"2026-08-07T18:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c17f622708a0a6b4ead0b318337e7450ea2d0ec5f9b575f99cece74357dd942b","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T00:54:51.757485Z","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":"2026-08-07T00:54:52.146980Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.06231","last_updated":"2021-03-10T18:06:05Z","snapshot_observed_at":"2026-08-07T14:12:39.849352Z","submitted_at":"2021-03-10T18:06:05Z","title":"Quantization-Guided Training for Compact TinyML Models","version":1},"cited_work":{"arxiv_id":"2103.06231","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.06231","snapshot_observed_at":"2026-08-07T00:54:52.146980Z","title":"Quantization-Guided Training for Compact TinyML Models","venue":"cs.LG","work_id":"7de8de36-3b8b-47bb-83e8-08252be67745","year":2021},"citing_paper":{"arxiv_id":"2506.12480","last_updated":"2025-06-14T12:43:47Z","snapshot_observed_at":"2026-08-07T00:46:20.012471Z","submitted_at":"2025-06-14T12:43:47Z","title":"Quantizing Small-Scale State-Space Models for Edge AI","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T00:54:51.757485Z"},"links":{"cited_paper":"/paper/2103.06231","citing_paper":"/paper/2506.12480"},"observation_digest":"sha256:7def5d065dbe3321414571f172efa35a21b10b57f6cda4da733af9d275267f03","observation_id":"cb65a649-b50e-48f4-869a-aaab61050ee7","resolution":{"observed_at":"2026-08-07T00:54:52.153291Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2103.06231/citation-record","integrity":"/paper/2103.06231/integrity","json":"/paper/2103.06231/citation-record.json","paper":"/paper/2103.06231"},"outbound":[],"paper":{"arxiv_id":"2103.06231","last_updated":"2021-03-10T18:06:05Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T14:12:39.849352Z","submitted_at":"2021-03-10T18:06:05Z","title":"Quantization-Guided Training for Compact TinyML Models"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2103.06231."}