{"as_of":"2026-08-21T22:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:90651447622375699b9b438c13675bcbe47290fa62d127da2c7e16eff869e707","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:10:48.396033Z","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":"2026-07-04T14:49:54.419383Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.00039","last_updated":"2024-03-24T02:45:34Z","snapshot_observed_at":"2026-08-16T14:07:02.297556Z","submitted_at":"2024-03-24T02:45:34Z","title":"MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00039","snapshot_observed_at":"2026-08-15T23:10:48.396033Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.05413","last_updated":"2025-05-08T16:54:48Z","snapshot_observed_at":"2026-08-15T23:02:04.962329Z","submitted_at":"2025-05-08T16:54:48Z","title":"DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T23:10:48.396033Z"},"links":{"cited_paper":"/paper/2404.00039","citing_paper":"/paper/2505.05413"},"observation_digest":"sha256:d3a7a5a70bca6a51f6aa98e4a253da647de07108e129fe8b7f849cade9bcf9d1","observation_id":"d49f0c93-d3f5-4622-97fe-6284be6a7a15","resolution":{"observed_at":"2026-08-15T23:10:48.396033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00039","last_updated":"2024-03-24T02:45:34Z","snapshot_observed_at":"2026-08-16T14:07:02.297556Z","submitted_at":"2024-03-24T02:45:34Z","title":"MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems","version":1},"cited_work":{"arxiv_id":"2404.00039","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00039","snapshot_observed_at":"2026-07-04T14:49:54.419383Z","title":null,"venue":null,"work_id":"916af700-cc85-4d0b-80b7-5e2d74fef678","year":2024},"citing_paper":{"arxiv_id":"2606.26547","last_updated":"2026-06-25T02:46:49Z","snapshot_observed_at":"2026-08-14T07:13:35.454431Z","submitted_at":"2026-06-25T02:46:49Z","title":"Compiler-Driven Approximation Tuning for Hyperdimensional Computing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T02:40:56.223199Z"},"links":{"cited_paper":"/paper/2404.00039","citing_paper":"/paper/2606.26547"},"observation_digest":"sha256:672838800cabf547d90de33c9dc77132abc388606ce5d417a0b3ab840670345e","observation_id":"3a924e51-c085-402d-932d-46a779e4c702","resolution":{"observed_at":"2026-07-04T14:49:54.423507Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2404.00039/citation-record","integrity":"/paper/2404.00039/integrity","json":"/paper/2404.00039/citation-record.json","paper":"/paper/2404.00039"},"outbound":[],"paper":{"arxiv_id":"2404.00039","last_updated":"2024-03-24T02:45:34Z","latest_version":1,"primary_category":"cs.PF","snapshot_observed_at":"2026-08-16T14:07:02.297556Z","submitted_at":"2024-03-24T02:45:34Z","title":"MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.00039."}