{"as_of":"2026-08-15T23:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:67ec952e4b30c04239a0227932525739d5fd795cd403e3d59e38ee8bc111a0ca","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:13:39.796181Z","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":"2026-05-19T09:02:14.518612Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.12200","last_updated":"2024-06-04T06:39:23Z","snapshot_observed_at":"2026-08-15T15:24:35.168536Z","submitted_at":"2024-01-22T18:39:40Z","title":"APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12200","snapshot_observed_at":"2026-08-11T14:57:27.645472Z","title":"Apt: Adaptive pruning and tuning pretrained language models for efficient training and inference","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11494","last_updated":"2024-12-16T07:09:46Z","snapshot_observed_at":"2026-08-12T15:20:29.441453Z","submitted_at":"2024-12-16T07:09:46Z","title":"FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-11T14:57:27.645472Z"},"links":{"cited_paper":"/paper/2401.12200","citing_paper":"/paper/2412.11494"},"observation_digest":"sha256:160a4ce7ee36a3d8cf33751db5c31ba7e64484aa75b9ff7117d177aec1a21f2e","observation_id":"39140dc0-ce8b-45ab-80bf-e1d8715e6492","resolution":{"observed_at":"2026-08-11T14:57:27.645472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12200","last_updated":"2024-06-04T06:39:23Z","snapshot_observed_at":"2026-08-15T15:24:35.168536Z","submitted_at":"2024-01-22T18:39:40Z","title":"APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12200","snapshot_observed_at":"2026-08-11T13:37:39.109971Z","title":"Apt: Adaptive pruning and tun- ing pretrained language models for efficient training and inference","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.12951","last_updated":"2024-12-17T14:33:05Z","snapshot_observed_at":"2026-08-15T07:14:19.114051Z","submitted_at":"2024-12-17T14:33:05Z","title":"FineGates: LLMs Finetuning with Compression using Stochastic Gates","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T13:37:39.109971Z"},"links":{"cited_paper":"/paper/2401.12200","citing_paper":"/paper/2412.12951"},"observation_digest":"sha256:a31510e23f35ac0019a19de7c808d15b717b03dfbad41cc1c54163e8b8f8f8e6","observation_id":"cbffbc6e-f309-4b74-9466-41507d492341","resolution":{"observed_at":"2026-08-11T13:37:39.109971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12200","last_updated":"2024-06-04T06:39:23Z","snapshot_observed_at":"2026-08-15T15:24:35.168536Z","submitted_at":"2024-01-22T18:39:40Z","title":"APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12200","snapshot_observed_at":"2026-08-15T20:13:39.796181Z","title":"Apt: Adaptive pruning and tuning pretrained language models for efficient training and inference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13840","last_updated":"2025-05-20T02:27:08Z","snapshot_observed_at":"2026-08-15T20:07:00.293706Z","submitted_at":"2025-05-20T02:27:08Z","title":"EfficientLLM: Efficiency in Large Language Models","version":1},"reference_index":299,"source":"arxiv_source","source_observed_at":"2026-08-15T20:13:39.796181Z"},"links":{"cited_paper":"/paper/2401.12200","citing_paper":"/paper/2505.13840"},"observation_digest":"sha256:3430439c939a36653228e6e1393511370c93dfb607bee8415d6e13314621f706","observation_id":"b290b01b-bd81-43d9-bc2e-c991d22d2838","resolution":{"observed_at":"2026-08-15T20:13:39.796181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12200","last_updated":"2024-06-04T06:39:23Z","snapshot_observed_at":"2026-08-15T15:24:35.168536Z","submitted_at":"2024-01-22T18:39:40Z","title":"APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference","version":2},"cited_work":{"arxiv_id":"2401.12200","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.12200","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Apt: Adaptive pruning and tuning pretrained language models for efficient training and inference","venue":null,"work_id":"693a2061-c64c-4d95-81da-9351597fd2b2","year":null},"citing_paper":{"arxiv_id":"2506.12876","last_updated":"2026-05-13T04:56:46Z","snapshot_observed_at":"2026-08-01T16:23:25.586402Z","submitted_at":"2025-06-15T15:02:59Z","title":"MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-19T09:01:16.991413Z"},"links":{"cited_paper":"/paper/2401.12200","citing_paper":"/paper/2506.12876"},"observation_digest":"sha256:7a8bdb4f663fd1dbdc2c5bdceff572f312d85ea02f36e8f372770b95bea6953c","observation_id":"aee10fc1-99f2-4a51-8972-51ec7728b6b9","resolution":{"observed_at":"2026-05-19T09:02:14.522226Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2401.12200/citation-record","integrity":"/paper/2401.12200/integrity","json":"/paper/2401.12200/citation-record.json","paper":"/paper/2401.12200"},"outbound":[],"paper":{"arxiv_id":"2401.12200","last_updated":"2024-06-04T06:39:23Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T15:24:35.168536Z","submitted_at":"2024-01-22T18:39:40Z","title":"APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and 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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2401.12200."}