{"as_of":"2026-08-16T02:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aec3c0592291ed678f1c6734fa9cb59238a6b695628043d7597c8bcda3c8376f","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:35:43.474963Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.14711","last_updated":"2023-09-18T17:50:21Z","snapshot_observed_at":"2026-08-13T10:25:35.505406Z","submitted_at":"2023-08-28T17:11:41Z","title":"Fast Feedforward Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.14711","snapshot_observed_at":"2026-08-06T20:35:43.474963Z","title":"and Wattenhofer, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02550","last_updated":"2025-07-03T11:49:56Z","snapshot_observed_at":"2026-08-15T11:01:39.337934Z","submitted_at":"2025-07-03T11:49:56Z","title":"Position: A Theory of Deep Learning Must Include Compositional Sparsity","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T20:35:43.474963Z"},"links":{"cited_paper":"/paper/2308.14711","citing_paper":"/paper/2507.02550"},"observation_digest":"sha256:ef70c810399b2c11c406eced12e7c3133606c5e3ef71cf3f03bc79a0d1490f2c","observation_id":"6075bcdb-7196-458f-a53a-448fb0cb73ea","resolution":{"observed_at":"2026-08-06T20:35:43.474963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.14711","last_updated":"2023-09-18T17:50:21Z","snapshot_observed_at":"2026-08-13T10:25:35.505406Z","submitted_at":"2023-08-28T17:11:41Z","title":"Fast Feedforward Networks","version":2},"cited_work":{"arxiv_id":"2308.14711","doi":"10.48550/arxiv.2308.14711","metadata_source":"arxiv_reference","pith_arxiv_id":"2308.14711","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fast feedforward networks","venue":"arXiv (Cornell University)","work_id":"d9f801b3-0bf0-4a82-b894-73bd365055c0","year":2023},"citing_paper":{"arxiv_id":"2605.04069","last_updated":"2026-04-12T19:18:19Z","snapshot_observed_at":"2026-07-06T23:16:54.673178Z","submitted_at":"2026-04-12T19:18:19Z","title":"LAWS: Learning from Actual Workloads Symbolically -- A Self-Certifying Parametrized Cache Architecture for Neural Inference, Robotics, and Edge Deployment","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T15:43:14.768649Z"},"links":{"cited_paper":"/paper/2308.14711","citing_paper":"/paper/2605.04069"},"observation_digest":"sha256:a8c6cde35c18cc9381992f4e3840de4cea2504395798f055603e391499cd6162","observation_id":"c5286010-d432-4e66-8f60-6aecb6989a70","resolution":{"observed_at":"2026-05-11T09:56:05.269113Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.14711","last_updated":"2023-09-18T17:50:21Z","snapshot_observed_at":"2026-08-13T10:25:35.505406Z","submitted_at":"2023-08-28T17:11:41Z","title":"Fast Feedforward Networks","version":2},"cited_work":{"arxiv_id":"2308.14711","doi":"10.48550/arxiv.2308.14711","metadata_source":"arxiv_reference","pith_arxiv_id":"2308.14711","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fast feedforward networks","venue":"arXiv (Cornell University)","work_id":"d9f801b3-0bf0-4a82-b894-73bd365055c0","year":2023},"citing_paper":{"arxiv_id":"2606.30516","last_updated":"2026-06-29T16:24:17Z","snapshot_observed_at":"2026-08-15T00:23:06.618897Z","submitted_at":"2026-06-29T16:24:17Z","title":"HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T06:27:05.897385Z"},"links":{"cited_paper":"/paper/2308.14711","citing_paper":"/paper/2606.30516"},"observation_digest":"sha256:9704d3bfdff3e7fc3167b04c421bb65e0797abb6c9416945004bf71a4b0c4c71","observation_id":"2e2eee0f-56ee-406b-b9bc-dd33889c46c4","resolution":{"observed_at":"2026-06-30T06:34:18.278049Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.14711","last_updated":"2023-09-18T17:50:21Z","snapshot_observed_at":"2026-08-13T10:25:35.505406Z","submitted_at":"2023-08-28T17:11:41Z","title":"Fast Feedforward Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.14711","snapshot_observed_at":"2026-07-14T06:26:24.512653Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11183","last_updated":"2026-07-14T08:45:42Z","snapshot_observed_at":"2026-08-15T04:55:43.276315Z","submitted_at":"2026-07-13T07:28:55Z","title":"Amplitude-Only FFN Intervention for Tool-Structured LLM Inference Method: Gated Evaluation Protocol, and Cross-Model Empirical Results","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T06:26:24.512653Z"},"links":{"cited_paper":"/paper/2308.14711","citing_paper":"/paper/2607.11183"},"observation_digest":"sha256:f4d9e49955ed4e0ad6f37b34693b65ab98ae8afc613ab7759c53a5df15b55ca6","observation_id":"f7f05fb8-35ea-4b27-8153-8468bd6900d6","resolution":{"observed_at":"2026-07-14T06:26:24.512653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.14711","last_updated":"2023-09-18T17:50:21Z","snapshot_observed_at":"2026-08-13T10:25:35.505406Z","submitted_at":"2023-08-28T17:11:41Z","title":"Fast Feedforward Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.14711","snapshot_observed_at":"2026-07-15T08:58:21.654258Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11183","last_updated":"2026-07-14T08:45:42Z","snapshot_observed_at":"2026-08-15T04:55:43.276315Z","submitted_at":"2026-07-13T07:28:55Z","title":"Amplitude-Only FFN Intervention for Tool-Structured LLM Inference Method: Gated Evaluation Protocol, and Cross-Model Empirical Results","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-15T08:58:21.654258Z"},"links":{"cited_paper":"/paper/2308.14711","citing_paper":"/paper/2607.11183"},"observation_digest":"sha256:e9afbdb0a7604a2198b91ae05df5cda101e7127c315b1791ff25c9b691e08c07","observation_id":"59854b7d-b2c3-4865-b66e-6dc50c122360","resolution":{"observed_at":"2026-07-15T08:58:21.654258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2308.14711/citation-record","integrity":"/paper/2308.14711/integrity","json":"/paper/2308.14711/citation-record.json","paper":"/paper/2308.14711"},"outbound":[],"paper":{"arxiv_id":"2308.14711","last_updated":"2023-09-18T17:50:21Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T10:25:35.505406Z","submitted_at":"2023-08-28T17:11:41Z","title":"Fast Feedforward Networks"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2308.14711."}