{"as_of":"2026-08-23T05:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a65ab8bd0527da2ac26ea8ac5ccf73a98a167ddb8da51f76a6dfa8aef66aeb6d","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":"2026-08-22T06:32:14.747728+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":"2026-08-06T21:15:08.374201Z","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":"2026-08-06T21:07:32.843788Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2112.02191","last_updated":"2021-12-03T23:06:57Z","snapshot_observed_at":"2026-08-16T17:37:09.026234Z","submitted_at":"2021-12-03T23:06:57Z","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.02191","snapshot_observed_at":"2026-08-06T21:15:08.374201Z","title":"Nn-lut: Neural approximation of non-linear operations for efficient transformer inference,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00797","last_updated":"2025-07-01T14:30:31Z","snapshot_observed_at":"2026-08-18T07:56:44.662733Z","submitted_at":"2025-07-01T14:30:31Z","title":"VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:15:08.374201Z"},"links":{"cited_paper":"/paper/2112.02191","citing_paper":"/paper/2507.00797"},"observation_digest":"sha256:1b8cc688419a8d8e0d5c444a403c94654d0a9fd190de467c450f774104b442ec","observation_id":"4e5f3e1d-e7ca-41e7-b216-ccac6a4f76db","resolution":{"observed_at":"2026-08-06T21:15:08.374201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02191","last_updated":"2021-12-03T23:06:57Z","snapshot_observed_at":"2026-08-16T17:37:09.026234Z","submitted_at":"2021-12-03T23:06:57Z","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference","version":1},"cited_work":{"arxiv_id":"2112.02191","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.02191","snapshot_observed_at":"2026-08-06T21:07:32.843788Z","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference","venue":"cs.LG","work_id":"146992b3-5239-4cf7-b0a6-9ad5d032fc93","year":2021},"citing_paper":{"arxiv_id":"2507.01309","last_updated":"2025-07-02T02:53:43Z","snapshot_observed_at":"2026-08-21T15:01:50.588525Z","submitted_at":"2025-07-02T02:53:43Z","title":"SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T21:07:32.614534Z"},"links":{"cited_paper":"/paper/2112.02191","citing_paper":"/paper/2507.01309"},"observation_digest":"sha256:0ccc49d6f24bc7cfea7de527da1561aec3751621c40d97719e6d47e536f3eaaf","observation_id":"342e2629-7c39-4adb-9b1d-5ce67789ce0b","resolution":{"observed_at":"2026-08-06T21:07:32.945823Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2112.02191/citation-record","integrity":"/paper/2112.02191/integrity","json":"/paper/2112.02191/citation-record.json","paper":"/paper/2112.02191"},"outbound":[],"paper":{"arxiv_id":"2112.02191","last_updated":"2021-12-03T23:06:57Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:37:09.026234Z","submitted_at":"2021-12-03T23:06:57Z","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.02191."}