{"as_of":"2026-08-08T13:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0e53374b8559a7595f983efdf4f1639cfca66962a2280e282407c360d5e290ef","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T00:55:50.120232Z","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":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-07T12:09:07.514859Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00479","last_updated":"2025-05-31T09:10:43Z","snapshot_observed_at":"2026-08-08T13:10:06.261113Z","submitted_at":"2025-05-31T09:10:43Z","title":"EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T12:09:07.514859Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2506.00479"},"observation_digest":"sha256:e7bf312a350022f7b5ce5dc06fe3c1c3b47c781c91a7e072b110b946dc0caa9a","observation_id":"02e740a3-e6a5-40af-9e5b-e2df760632d3","resolution":{"observed_at":"2026-08-07T12:09:07.514859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-07T11:57:29.197574Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.01049","last_updated":"2025-06-01T15:30:37Z","snapshot_observed_at":"2026-08-07T22:23:46.839915Z","submitted_at":"2025-06-01T15:30:37Z","title":"Taming LLMs by Scaling Learning Rates with Gradient Grouping","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-07T11:57:29.197574Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2506.01049"},"observation_digest":"sha256:90d3539ef0dac5cf9ae4124495f41e11d97d8df075431ca2e8b05004184f32e4","observation_id":"e5acd949-8072-4ba1-91a2-67b0f7a43217","resolution":{"observed_at":"2026-08-07T11:57:29.197574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-06T23:57:25.601970Z","title":"arXiv preprint arXiv:2408.15881 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15681","last_updated":"2026-06-25T14:33:27Z","snapshot_observed_at":"2026-08-08T08:54:57.204006Z","submitted_at":"2025-06-18T17:59:49Z","title":"GenRecal: Generation after Recalibration from Large to Small Vision-Language Models","version":4},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T23:57:25.601970Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2506.15681"},"observation_digest":"sha256:9f4a9c3797889ea224f2c5850444a3044e219e499aba51e51b6c5830bf048269","observation_id":"d467dccd-ba5d-42c6-be9e-3dd45878b382","resolution":{"observed_at":"2026-08-06T23:57:25.601970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-03T21:09:24.726089Z","title":"Llava-mod: Making llava tiny via moe knowledge distillation.arXiv preprint arXiv:2408.15881, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.16672","last_updated":"2026-06-09T22:19:41Z","snapshot_observed_at":"2026-08-06T04:41:40.984877Z","submitted_at":"2025-11-20T18:59:54Z","title":"EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T21:09:24.726089Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2511.16672"},"observation_digest":"sha256:79d895c90c9e3d1655e1095c9342c360cf545bcab154e65da2002122d4b43010","observation_id":"99b1b392-0dc2-4e2c-9b1c-36795ad393c0","resolution":{"observed_at":"2026-08-03T21:09:24.726089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":"2408.15881","doi":"10.48550/arxiv.2408.15881","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llava-mod: Making llava tiny via moe knowledge distillation.arXiv preprint arXiv:2408.15881","venue":"arXiv (Cornell University)","work_id":"9dbe8839-1c57-413a-84ec-643f71b1c1fb","year":2024},"citing_paper":{"arxiv_id":"2604.14629","last_updated":"2026-04-16T05:13:57Z","snapshot_observed_at":"2026-07-06T23:02:22.790426Z","submitted_at":"2026-04-16T05:13:57Z","title":"Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T11:23:46.371799Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2604.14629"},"observation_digest":"sha256:3c32273e8dbcfb59f09b0e073596c836fc33b60ca31f7ab73199bf8ed2f9608f","observation_id":"c331b021-49dd-449b-9275-653f29f25331","resolution":{"observed_at":"2026-05-10T11:25:18.526409Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":"2408.15881","doi":"10.48550/arxiv.2408.15881","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llava-mod: Making llava tiny via moe knowledge distillation.arXiv preprint arXiv:2408.15881","venue":"arXiv (Cornell University)","work_id":"9dbe8839-1c57-413a-84ec-643f71b1c1fb","year":2024},"citing_paper":{"arxiv_id":"2604.21027","last_updated":"2026-08-02T16:15:42Z","snapshot_observed_at":"2026-08-06T23:24:26.820832Z","submitted_at":"2026-04-22T19:18:36Z","title":"HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering","version":1},"reference_index":214,"source":"arxiv_source","source_observed_at":"2026-05-09T23:51:47.724033Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2604.21027"},"observation_digest":"sha256:0eed3aeb1d441f64607af59741bb2d1452f5cdbf8247a23fa2acc3565cd907e3","observation_id":"5b68943e-0271-48d1-8a18-f01ccad88700","resolution":{"observed_at":"2026-05-09T23:54:45.348826Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":"2408.15881","doi":"10.48550/arxiv.2408.15881","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llava-mod: Making llava tiny via moe knowledge distillation.arXiv preprint arXiv:2408.15881","venue":"arXiv (Cornell University)","work_id":"9dbe8839-1c57-413a-84ec-643f71b1c1fb","year":2024},"citing_paper":{"arxiv_id":"2606.05718","last_updated":"2026-06-04T05:18:13Z","snapshot_observed_at":"2026-08-02T23:26:06.327596Z","submitted_at":"2026-06-04T05:18:13Z","title":"ViCuR: Visual Cues as Recoverable Privilege for Multimodal On-Policy Distillation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T01:59:15.154873Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2606.05718"},"observation_digest":"sha256:ddd0750d385290469af33ca3bb917393cddfd596dd4f977f265d1250465db7bd","observation_id":"52144856-6287-4cda-93d6-004ee972db3e","resolution":{"observed_at":"2026-07-02T12:36:57.017799Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15881","snapshot_observed_at":"2026-08-08T00:55:50.120232Z","title":"Llava-mod: Making llava tiny via moe knowledge distillation.arXiv preprint arXiv:2408.15881,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03112","last_updated":"2026-08-04T04:32:48Z","snapshot_observed_at":"2026-08-08T00:50:14.056001Z","submitted_at":"2026-08-04T04:32:48Z","title":"Adaptive Two-Stage Visual Token Pruning for Efficient Inference in Video-Language Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T00:55:50.120232Z"},"links":{"cited_paper":"/paper/2408.15881","citing_paper":"/paper/2608.03112"},"observation_digest":"sha256:7c75b46cb14cf23781c8aa1956bbd45fc5617807c5ee1c2eb517508332956fdd","observation_id":"afb7bdd5-f0d4-4314-9070-1171eb84c3f4","resolution":{"observed_at":"2026-08-08T00:55:50.120232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.15881/citation-record","integrity":"/paper/2408.15881/integrity","json":"/paper/2408.15881/citation-record.json","paper":"/paper/2408.15881"},"outbound":[],"paper":{"arxiv_id":"2408.15881","last_updated":"2024-10-23T09:52:23Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T18:22:08.247118Z","submitted_at":"2024-08-28T15:52:23Z","title":"LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillation"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.15881."}