{"as_of":"2026-08-10T06:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dfaa54afa906aa026d3beff76ff43b5c90fa68d6e58dfddd7f06cde692dad0df","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T04:30:14.296106Z","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":"2026-06-29T21:13:59.795139Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","version":1},"cited_work":{"arxiv_id":"2412.00447","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.00447","snapshot_observed_at":"2026-06-29T21:13:59.795139Z","title":"Atp-llava: Adaptive token pruning for large vision language models","venue":null,"work_id":"570f19d0-0d55-428e-8a5c-bc458f6585f9","year":2024},"citing_paper":{"arxiv_id":"2503.14075","last_updated":"2026-04-10T01:08:12Z","snapshot_observed_at":"2026-07-06T20:54:36.522651Z","submitted_at":"2025-03-18T09:52:45Z","title":"Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-22T23:58:57.819555Z"},"links":{"cited_paper":"/paper/2412.00447","citing_paper":"/paper/2503.14075"},"observation_digest":"sha256:f591cebc27373fd52c1eef10da38b618b9f33f2c390ac15027733dc0647d3246","observation_id":"9934be60-afea-4477-a28c-c687066f5b28","resolution":{"observed_at":"2026-05-23T00:02:17.747172Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00447","snapshot_observed_at":"2026-08-07T15:42:56.173290Z","title":"Atp-llava: Adaptive token pruning for large vision language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14231","last_updated":"2025-05-20T11:40:43Z","snapshot_observed_at":"2026-08-07T15:35:48.142895Z","submitted_at":"2025-05-20T11:40:43Z","title":"UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:56.173290Z"},"links":{"cited_paper":"/paper/2412.00447","citing_paper":"/paper/2505.14231"},"observation_digest":"sha256:7192c15b66761cb56c8684e81d30423b1493f440c87ad1bd128f54727d13917e","observation_id":"fe73a043-70c3-4c6f-b31b-5245c3db6f91","resolution":{"observed_at":"2026-08-07T15:42:56.173290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","version":1},"cited_work":{"arxiv_id":"2412.00447","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.00447","snapshot_observed_at":"2026-06-29T21:13:59.795139Z","title":"Atp-llava: Adaptive token pruning for large vision language models","venue":null,"work_id":"570f19d0-0d55-428e-8a5c-bc458f6585f9","year":2024},"citing_paper":{"arxiv_id":"2505.18719","last_updated":"2025-05-24T14:42:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-24T14:42:51Z","title":"VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-16T12:55:40.245908Z"},"links":{"cited_paper":"/paper/2412.00447","citing_paper":"/paper/2505.18719"},"observation_digest":"sha256:8bf9a391d5c5fcafd603ae0cb1d27dd822b914cdf4cc591fd8b99a5df5b1528e","observation_id":"5f9f9108-cba8-4174-8c38-58b4ea014579","resolution":{"observed_at":"2026-05-16T12:55:40.366251Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00447","snapshot_observed_at":"2026-08-06T22:24:35.188337Z","title":"Atp-llava: Adaptive token pruning for large vision language models.ArXiv, abs/2412.00447, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21862","last_updated":"2025-06-27T02:29:58Z","snapshot_observed_at":"2026-08-09T10:29:21.701927Z","submitted_at":"2025-06-27T02:29:58Z","title":"LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-06T22:24:35.188337Z"},"links":{"cited_paper":"/paper/2412.00447","citing_paper":"/paper/2506.21862"},"observation_digest":"sha256:7658bd0c1808312070a5bb43dd17251a604c431ede53b4e9e3c13b2eb2b8a2a9","observation_id":"a93b1bf0-9723-4142-98da-f09977319f22","resolution":{"observed_at":"2026-08-06T22:24:35.188337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00447","snapshot_observed_at":"2026-08-06T10:55:18.362610Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.23362","last_updated":"2025-07-31T09:17:53Z","snapshot_observed_at":"2026-08-08T14:50:30.917971Z","submitted_at":"2025-07-31T09:17:53Z","title":"Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T10:55:18.362610Z"},"links":{"cited_paper":"/paper/2412.00447","citing_paper":"/paper/2507.23362"},"observation_digest":"sha256:d046a39167e27bb91c88285e18a617716aec2bf2ad11e3ccf60c4a4c5b6f413b","observation_id":"73c887e9-f33a-4fb4-8e9a-3a97ec191a32","resolution":{"observed_at":"2026-08-06T10:55:18.362610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","version":1},"cited_work":{"arxiv_id":"2412.00447","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.00447","snapshot_observed_at":"2026-06-29T21:13:59.795139Z","title":"Atp-llava: Adaptive token pruning for large vision language models","venue":null,"work_id":"570f19d0-0d55-428e-8a5c-bc458f6585f9","year":2024},"citing_paper":{"arxiv_id":"2605.25842","last_updated":"2026-05-29T07:58:50Z","snapshot_observed_at":"2026-07-06T23:35:46.157653Z","submitted_at":"2026-05-25T13:36:46Z","title":"MuCRASP: Multimodal Chain-of-thought Reasoning aware Structured Pruning","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-29T21:12:45.941244Z"},"links":{"cited_paper":"/paper/2412.00447","citing_paper":"/paper/2605.25842"},"observation_digest":"sha256:db0cddb5e0d49b2c413b7e3ea8bcb0c9ee36663dced7a3db5ea4bb6774758d06","observation_id":"941a8a6c-ea1c-4128-b28b-4ff1bedf8ecb","resolution":{"observed_at":"2026-06-29T21:13:59.796821Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00447","snapshot_observed_at":"2026-08-10T04:30:14.296106Z","title":"arXiv preprint arXiv:2412.00447 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06411","last_updated":"2026-08-04T05:44:07Z","snapshot_observed_at":"2026-08-10T06:09:53.315342Z","submitted_at":"2026-08-04T05:44:07Z","title":"Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T04:30:14.296106Z"},"links":{"cited_paper":"/paper/2412.00447","citing_paper":"/paper/2608.06411"},"observation_digest":"sha256:2d76f0853a76193c82678afdfe639f5804a7fd026fcb5c53cdcec83fd82f95e6","observation_id":"5f172363-a22b-4c8d-b29a-583864f922bb","resolution":{"observed_at":"2026-08-10T04:30:14.296106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.00447/citation-record","integrity":"/paper/2412.00447/integrity","json":"/paper/2412.00447/citation-record.json","paper":"/paper/2412.00447"},"outbound":[],"paper":{"arxiv_id":"2412.00447","last_updated":"2024-11-30T11:42:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T22:02:40.535467Z","submitted_at":"2024-11-30T11:42:35Z","title":"ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2412.00447."}