{"as_of":"2026-08-17T18:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e47814e033c537801d528e97ae0b5734c2ea74a8592ae436b069b6139c2ab0b2","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":"2026-08-17T06:30:58.91139+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":"2026-08-02T11:54:03.411984Z","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":"2026-05-22T13:21:35.649075Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.15618","last_updated":"2025-02-21T17:41:21Z","snapshot_observed_at":"2026-08-16T12:56:17.331123Z","submitted_at":"2025-02-21T17:41:21Z","title":"Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing","version":1},"cited_work":{"arxiv_id":"2502.15618","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.15618","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Probe pruning: Accelerating llms through dynamic pruning via model-probing","venue":null,"work_id":"e6bc4f36-3723-4e43-84a5-3ae1d9bc7ce1","year":null},"citing_paper":{"arxiv_id":"2505.17138","last_updated":"2026-05-18T17:05:12Z","snapshot_observed_at":"2026-08-17T13:25:30.263845Z","submitted_at":"2025-05-22T06:12:42Z","title":"RAP: Runtime Adaptive Pruning for LLM Inference","version":5},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-22T13:20:41.739571Z"},"links":{"cited_paper":"/paper/2502.15618","citing_paper":"/paper/2505.17138"},"observation_digest":"sha256:b70598ece019d6e5a9e901e2d0a27a1f5574e744cdad27dd499c288fa2ba9a51","observation_id":"bb593e52-c73e-4260-b528-31a4ceb3afed","resolution":{"observed_at":"2026-05-22T13:21:35.651616Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15618","last_updated":"2025-02-21T17:41:21Z","snapshot_observed_at":"2026-08-16T12:56:17.331123Z","submitted_at":"2025-02-21T17:41:21Z","title":"Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15618","snapshot_observed_at":"2026-08-02T11:54:03.411984Z","title":"Probe pruning: Accelerating llms through dynamic pruning via model probing.arXiv preprint arXiv:2502.15618, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.22587","last_updated":"2026-06-09T17:02:14Z","snapshot_observed_at":"2026-08-14T10:50:01.768953Z","submitted_at":"2026-06-09T17:02:14Z","title":"TriSP: Tri-Signal Structured Pruning for Large Language Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T11:54:03.411984Z"},"links":{"cited_paper":"/paper/2502.15618","citing_paper":"/paper/2607.22587"},"observation_digest":"sha256:8ca151432b65b7a608dacb64c7578755d23831f0c10959dd7eececb142348654","observation_id":"7f341017-b36d-4d01-8a17-7924c9ad5817","resolution":{"observed_at":"2026-08-02T11:54:03.411984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.15618/citation-record","integrity":"/paper/2502.15618/integrity","json":"/paper/2502.15618/citation-record.json","paper":"/paper/2502.15618"},"outbound":[],"paper":{"arxiv_id":"2502.15618","last_updated":"2025-02-21T17:41:21Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T12:56:17.331123Z","submitted_at":"2025-02-21T17:41:21Z","title":"Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.15618."}