{"as_of":"2026-08-06T16:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b491b6a435df794d686ca3860047127ed301064c8d4e8723cae279fbf3abc6a7","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":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":37,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:48:33.180292Z","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":"2026-07-04T13:09:50.847114Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2412.04468","last_updated":"2026-04-25T07:16:42Z","snapshot_observed_at":"2026-08-03T08:48:57.969106Z","submitted_at":"2024-12-05T18:59:55Z","title":"NVILA: Efficient Frontier Visual Language Models","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-23T07:42:22.478647Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2412.04468"},"observation_digest":"sha256:ce676a09d036081fdfa21f80204353e96e283cdf6b257b0e55ff077eef8bef61","observation_id":"e0925f8d-efec-4110-8251-c6b1daa0f532","resolution":{"observed_at":"2026-05-23T07:42:43.235839Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2412.05271","last_updated":"2025-09-26T12:52:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-06T18:57:08Z","title":"Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling","version":5},"reference_index":160,"source":"pdf_text","source_observed_at":"2026-05-10T13:23:57.588851Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2412.05271"},"observation_digest":"sha256:f0be0dcd3ad979d288a27b8e49329a37a4e11384391c192d9546e04b15868d41","observation_id":"a0f969e2-d8f5-486e-bdf4-ec96e7c62893","resolution":{"observed_at":"2026-05-10T13:23:57.747383Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2501.02955","last_updated":"2026-05-12T15:02:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-06T11:57:38Z","title":"MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-23T05:44:31.546843Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2501.02955"},"observation_digest":"sha256:e2821e76783cb46a14eddcd1fc51d9b82774fab9f0e3eecd5a7f297c8cab58fb","observation_id":"25818a3a-9cf6-4172-babf-0805141c6282","resolution":{"observed_at":"2026-05-23T05:45:28.369891Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2501.05067","last_updated":"2026-04-20T07:42:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-09T08:43:57Z","title":"LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-23T06:01:00.775721Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2501.05067"},"observation_digest":"sha256:9e98298697c3835968683b1a9bb475fe3bde48f6638abdf4c79927111bb3ac5b","observation_id":"96ae2de7-6e16-41b4-8353-9851ffc6d606","resolution":{"observed_at":"2026-05-23T06:02:37.421236Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2501.13106","last_updated":"2025-06-03T03:33:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T18:59:46Z","title":"VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-11T01:19:59.603343Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2501.13106"},"observation_digest":"sha256:a0c89072855f3fa2c0b822f1f0afa75a246bce529aff4a9198a4f68cde875340","observation_id":"5c2840ee-d5b0-4714-8645-3ddf4a95a080","resolution":{"observed_at":"2026-05-11T01:19:59.722872Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2504.10479","last_updated":"2025-04-19T03:47:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-14T17:59:25Z","title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","version":3},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-10T13:41:07.991012Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2504.10479"},"observation_digest":"sha256:a81f787f806da64d96ed12c83eff46e6f41c072a39f15746d9a266d96a322dd1","observation_id":"78546f28-51b5-41e9-b238-f48203b0cc7f","resolution":{"observed_at":"2026-05-10T13:41:08.381147Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2505.20279","last_updated":"2026-04-21T02:48:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-26T17:56:30Z","title":"VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction","version":5},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-19T12:54:01.013242Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2505.20279"},"observation_digest":"sha256:59b8a3bad0602489d00d3b733a6673073816b738729380554877e27f3cb354aa","observation_id":"c77b11ea-bf67-4cfe-b28a-8fe3ed3e4a48","resolution":{"observed_at":"2026-05-19T12:57:17.995688Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2505.23747","last_updated":"2026-05-19T02:23:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-29T17:59:04Z","title":"Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-16T08:34:36.824053Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2505.23747"},"observation_digest":"sha256:3932c1f9ef675032b6ff03e5c5d3aaff762b342c6a8547a78954e43d667adb67","observation_id":"5d95c883-b5b7-4803-9ca9-0b08cdbbd3b0","resolution":{"observed_at":"2026-05-16T08:34:36.933917Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2505.23747","last_updated":"2026-05-19T02:23:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-29T17:59:04Z","title":"Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-22T00:59:13.826054Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2505.23747"},"observation_digest":"sha256:1646e39e8a411a31cac5f73f727be2d80b0b902b2ea6aa9ea8c77e92ed20a5cb","observation_id":"bdd27281-da2f-4e45-85c9-51cdef6f0107","resolution":{"observed_at":"2026-05-22T01:00:51.255586Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2507.05920","last_updated":"2026-04-20T01:54:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-08T12:05:05Z","title":"High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-19T06:10:57.219445Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2507.05920"},"observation_digest":"sha256:d71a5768e031b95afd71da1cc64c014b7783ec37e07b5c5c6e651aaa3b865352","observation_id":"f5616df8-bba1-4eb2-a19e-34372d632036","resolution":{"observed_at":"2026-05-19T06:12:07.016238Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-06T15:48:33.180292Z","title":"Oryx mllm: On-demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15028","last_updated":"2025-07-20T16:30:33Z","snapshot_observed_at":"2026-08-06T15:40:12.400378Z","submitted_at":"2025-07-20T16:30:33Z","title":"Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:48:33.180292Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2507.15028"},"observation_digest":"sha256:d0a7c5df2970d4bea618a32f8cf8f87544a323d85962034a07c9c594ac38cced","observation_id":"ef22287e-6b45-409f-83b5-5da365626f32","resolution":{"observed_at":"2026-08-06T15:48:33.180292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-06T05:02:24.696608Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.02429","last_updated":"2025-08-04T13:49:03Z","snapshot_observed_at":"2026-08-06T05:02:23.017433Z","submitted_at":"2025-08-04T13:49:03Z","title":"Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T05:02:24.696608Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2508.02429"},"observation_digest":"sha256:56be25ad4c231d7aa902dc1120541bc53d5db8b262c0657d990741da900693ab","observation_id":"b95fbbae-b9c3-43f3-8cf9-7f2c29168dbd","resolution":{"observed_at":"2026-08-06T05:02:24.696608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-05T19:03:10.952463Z","title":"Oryx mllm: On-demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13692","last_updated":"2025-08-19T09:52:04Z","snapshot_observed_at":"2026-08-05T19:03:01.792773Z","submitted_at":"2025-08-19T09:52:04Z","title":"HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-05T19:03:10.952463Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2508.13692"},"observation_digest":"sha256:bcefa427a7f5e01ee609c8071a9d1591dea27aa2a304edfc4f15d6ff900c3349","observation_id":"88aa53dc-00ca-49c3-b000-fd682ddfb9e3","resolution":{"observed_at":"2026-08-05T19:03:10.952463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-05T17:13:07.966651Z","title":"arXiv preprint arXiv:2409.12961 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.16859","last_updated":"2025-08-23T01:10:29Z","snapshot_observed_at":"2026-08-05T17:13:04.263997Z","submitted_at":"2025-08-23T01:10:29Z","title":"Beyond Emotion Recognition: A Multi-Turn Multimodal Emotion Understanding and Reasoning Benchmark","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-05T17:13:07.966651Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2508.16859"},"observation_digest":"sha256:63903827976f282d75d9a609a63f59bee8a90f998f4d831e610cd4ea48c858c4","observation_id":"658ce853-647e-4c11-9558-ed2cef15393e","resolution":{"observed_at":"2026-08-05T17:13:07.966651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2508.18265","last_updated":"2025-08-27T14:39:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-08-25T17:58:17Z","title":"InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-10T11:58:58.660564Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2508.18265"},"observation_digest":"sha256:17a3e2b4a6fad884558439a706b258c77aad79e599ea87f45afe81a71102ad74","observation_id":"782a660f-92ca-44c3-bae5-d9aa665a2db4","resolution":{"observed_at":"2026-05-10T11:58:59.187761Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-04T20:32:56.684255Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08538","last_updated":"2025-09-11T11:14:00Z","snapshot_observed_at":"2026-08-04T20:32:53.384449Z","submitted_at":"2025-09-10T12:34:07Z","title":"MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T20:32:56.684255Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2509.08538"},"observation_digest":"sha256:fa1b99b15ede24fa8438cef09efc6b416dd9f1622bd847cb3415b8cea3ee3c76","observation_id":"8291684d-8999-4361-96c2-dda099eae3b5","resolution":{"observed_at":"2026-08-04T20:32:56.684255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-03T20:15:34.081170Z","title":"Oryx mllm: On-demand spatial-temporal understanding at arbitrary resolution.arXiv preprint arXiv:2409.12961, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.20644","last_updated":"2026-07-09T17:59:10Z","snapshot_observed_at":"2026-08-06T01:52:36.714650Z","submitted_at":"2025-11-25T18:59:02Z","title":"Vision-Language Memory for Spatial Reasoning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T20:15:34.081170Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2511.20644"},"observation_digest":"sha256:74b900dd0a6922c076791f8c9b23d3b1bbc442e7505c7887dc56c82d91cffa14","observation_id":"79b3cbe0-4f3b-4d46-b8a0-4f9896202924","resolution":{"observed_at":"2026-08-03T20:15:34.081170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2512.06673","last_updated":"2026-05-09T07:46:05Z","snapshot_observed_at":"2026-07-06T22:37:59.340166Z","submitted_at":"2025-12-07T06:11:15Z","title":"Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-17T00:54:53.789523Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2512.06673"},"observation_digest":"sha256:e8effdf5ac6902aec7fea20966d59599cbd3414e36b9b0f60a1e69e49c51fd69","observation_id":"470f744a-a41c-44e2-9102-190dfa7b92dd","resolution":{"observed_at":"2026-05-17T00:58:46.612461Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2512.21334","last_updated":"2026-04-10T15:00:46Z","snapshot_observed_at":"2026-08-03T08:10:30.527963Z","submitted_at":"2025-12-24T18:59:36Z","title":"Streaming Video Instruction Tuning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-16T19:44:11.032898Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2512.21334"},"observation_digest":"sha256:6fe45fae0ab5caf78ff344d0fb0e8eed5f8a4230b5ab3a60fe782bd415664969","observation_id":"011af0e0-4d45-4789-8742-668caa3108ca","resolution":{"observed_at":"2026-05-16T19:48:21.851540Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-03T09:12:18.037151Z","title":"InProceedings of the 2018 con- ference on empirical methods in natural language processing, pages 1369–1379","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2601.14569","last_updated":"2026-06-01T20:36:47Z","snapshot_observed_at":"2026-08-03T09:12:16.567435Z","submitted_at":"2026-01-21T01:10:42Z","title":"Social Caption: Evaluating Social Understanding in Multimodal Models","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-03T09:12:18.037151Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2601.14569"},"observation_digest":"sha256:610a3e6a131022f900e2d154c9903e549e593a1937e886f5e31bf10137de106c","observation_id":"30140cd9-f595-41b2-a384-a90470bebc5a","resolution":{"observed_at":"2026-08-03T09:12:18.037151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2602.20913","last_updated":"2026-04-15T16:09:22Z","snapshot_observed_at":"2026-08-02T12:38:41.181077Z","submitted_at":"2026-02-24T13:49:47Z","title":"LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-15T20:01:31.129959Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2602.20913"},"observation_digest":"sha256:f4f1a5b39fd9e4c6496fc10ef1a24a5f7070b632daa64594a7730bd7344f6d5a","observation_id":"a45b1f45-aa83-4b36-b733-fce8c1a1d963","resolution":{"observed_at":"2026-05-15T20:01:33.467842Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-08-02T20:14:05.151986Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution.arXiv preprint arXiv:2409.12961, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.00198","last_updated":"2026-07-01T00:40:56Z","snapshot_observed_at":"2026-08-02T20:14:03.060341Z","submitted_at":"2026-02-27T08:11:06Z","title":"Stateful Token Reduction for Long-Video Hybrid VLMs","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T20:14:05.151986Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2603.00198"},"observation_digest":"sha256:7e623eaac6fdc7ea2833500bf48c1a2a7a5812e74a12bef75d8bae6d8b84c62f","observation_id":"147c87bf-0038-4bb9-9f12-5d276f99f031","resolution":{"observed_at":"2026-08-02T20:14:05.151986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2604.03296","last_updated":"2026-03-28T00:54:19Z","snapshot_observed_at":"2026-07-06T22:52:29.705312Z","submitted_at":"2026-03-28T00:54:19Z","title":"3D-IDE: 3D Implicit Depth Emergent","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-14T22:34:04.833557Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2604.03296"},"observation_digest":"sha256:385ed41ee122ea70e7473e02d0b7cd7c4f4eda09d9e5438651203c66f52f89be","observation_id":"d74697c3-69bb-4ccf-9ece-b3f48fee6c83","resolution":{"observed_at":"2026-05-14T22:38:11.440996Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.05848","last_updated":"2026-05-08T13:46:44Z","snapshot_observed_at":"2026-07-06T23:18:22.301347Z","submitted_at":"2026-05-07T08:23:27Z","title":"VideoRouter: Query-Adaptive Dual Routing for Efficient Long-Video Understanding","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-08T14:48:39.444933Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.05848"},"observation_digest":"sha256:eae36906fca515406c2df847bd9529b4619acb0776577f0bf7e6287f55fe2aad","observation_id":"6df034c9-fb8f-4325-b306-44d5a82d595b","resolution":{"observed_at":"2026-05-11T18:41:10.088181Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.05848","last_updated":"2026-05-08T13:46:44Z","snapshot_observed_at":"2026-07-06T23:18:22.301347Z","submitted_at":"2026-05-07T08:23:27Z","title":"VideoRouter: Query-Adaptive Dual Routing for Efficient Long-Video Understanding","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-11T01:57:42.822121Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.05848"},"observation_digest":"sha256:54f7d921d3157ca75b77b38404ba788a5c6dede833447853265317962a332a15","observation_id":"6b37bb7d-5986-47b8-b93e-0126796083d6","resolution":{"observed_at":"2026-05-11T04:05:56.390960Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.06094","last_updated":"2026-05-22T03:20:58Z","snapshot_observed_at":"2026-08-01T22:07:44.933252Z","submitted_at":"2026-05-07T12:13:15Z","title":"VISD: Enhancing Video Reasoning via Structured Self-Distillation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-08T14:06:27.953376Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.06094"},"observation_digest":"sha256:2dfdc12c30e6472ba56b0b81a7dbd727166a45a4ec57bd1f7bb8712a2a407056","observation_id":"b90458eb-4969-41dc-b8a5-c7ce70b27228","resolution":{"observed_at":"2026-05-11T18:46:08.785397Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.06094","last_updated":"2026-05-22T03:20:58Z","snapshot_observed_at":"2026-08-01T22:07:44.933252Z","submitted_at":"2026-05-07T12:13:15Z","title":"VISD: Enhancing Video Reasoning via Structured Self-Distillation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-11T01:49:41.654207Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.06094"},"observation_digest":"sha256:7bded1f6a5ada915039cab206f094f397d1616a346607c55ee70f4804e16461e","observation_id":"343d0087-eb2b-4d90-97ed-fb4a66216af0","resolution":{"observed_at":"2026-05-11T01:50:51.275095Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.06094","last_updated":"2026-05-22T03:20:58Z","snapshot_observed_at":"2026-08-01T22:07:44.933252Z","submitted_at":"2026-05-07T12:13:15Z","title":"VISD: Enhancing Video Reasoning via Structured Self-Distillation","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-12T03:35:59.553683Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.06094"},"observation_digest":"sha256:69ec607d072c50fbd85c3be845d94a78b748f19da93a432b1fcf0f1c5040c16e","observation_id":"06b676f5-4ff0-45b7-925e-51e09e45b1d5","resolution":{"observed_at":"2026-05-12T07:11:28.052362Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.06094","last_updated":"2026-05-22T03:20:58Z","snapshot_observed_at":"2026-08-01T22:07:44.933252Z","submitted_at":"2026-05-07T12:13:15Z","title":"VISD: Enhancing Video Reasoning via Structured Self-Distillation","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-25T06:08:19.956833Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.06094"},"observation_digest":"sha256:7db8d727fb64e1fb1ffa609c1d80d472d5781b0c706bf15d1b5790b5f99bf3fc","observation_id":"c7e94571-41a5-4ef3-9a69-a6b7ab33be83","resolution":{"observed_at":"2026-05-25T06:10:24.064572Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.18018","last_updated":"2026-05-18T08:09:37Z","snapshot_observed_at":"2026-07-06T23:28:55.079333Z","submitted_at":"2026-05-18T08:09:37Z","title":"See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-20T12:10:54.874012Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.18018"},"observation_digest":"sha256:92b12899beff075b235fe2e5274a00038ecec7dbc5b61e69a1f18b6356c02fe7","observation_id":"45513e9d-92f6-4ba8-bc30-ada40750a6a2","resolution":{"observed_at":"2026-05-20T12:13:16.244738Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.21625","last_updated":"2026-05-20T18:36:57Z","snapshot_observed_at":"2026-07-06T23:32:01.202542Z","submitted_at":"2026-05-20T18:36:57Z","title":"Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-22T09:20:32.920925Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.21625"},"observation_digest":"sha256:849fd50ba8ce8d5412930b46f94fa23a5775e070fe3666958013e2cdee0c3ccf","observation_id":"8e43fe16-f2a6-4541-ad1d-63cb1e5b1499","resolution":{"observed_at":"2026-05-22T09:21:20.651921Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.25343","last_updated":"2026-05-25T01:57:43Z","snapshot_observed_at":"2026-07-06T23:35:16.647496Z","submitted_at":"2026-05-25T01:57:43Z","title":"Toward Native Multimodal Modeling: A Roadmap","version":1},"reference_index":211,"source":"pdf_text","source_observed_at":"2026-06-29T22:58:38.610609Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.25343"},"observation_digest":"sha256:8f50619ff8b2b0ba650ef8a277f10d5d1bbeea713c0cffa4ca8692e6f8f6bd5c","observation_id":"bf5a937f-eaf8-4524-924f-2ebead1d02e1","resolution":{"observed_at":"2026-06-29T23:04:01.738324Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2605.25979","last_updated":"2026-05-25T15:54:04Z","snapshot_observed_at":"2026-07-06T23:35:50.787324Z","submitted_at":"2026-05-25T15:54:04Z","title":"LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T22:12:05.365596Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2605.25979"},"observation_digest":"sha256:6224b167ffcf10b085ff1df22ffe1321dc7c4c14b658f015a492314bb3b3e5c8","observation_id":"9fc1f3ba-b6a6-45d6-87c6-c59c5d4c378d","resolution":{"observed_at":"2026-06-29T22:13:59.596700Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2606.06991","last_updated":"2026-06-05T07:29:20Z","snapshot_observed_at":"2026-07-06T23:46:42.693840Z","submitted_at":"2026-06-05T07:29:20Z","title":"Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T22:11:01.690237Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2606.06991"},"observation_digest":"sha256:abc8e8a5b51632c527dd98c99e525fc57f1ae6319ae124f430643c41d5cc42d1","observation_id":"687ae146-f056-42c1-bd57-d3b9df6a016b","resolution":{"observed_at":"2026-07-02T17:07:12.842034Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2606.17798","last_updated":"2026-06-16T11:18:05Z","snapshot_observed_at":"2026-07-06T23:53:21.186284Z","submitted_at":"2026-06-16T11:18:05Z","title":"LiveStarPro: Proactive Streaming Video Understanding with Hierarchical Memory for Long-Horizon Streams","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T01:12:46.295455Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2606.17798"},"observation_digest":"sha256:fd1e218636e88d75eceb3e21ba9ae7cfe6affbebb80baea4c92376801e2169d9","observation_id":"89a1a066-f3e4-47fc-9d6e-9f3370537c85","resolution":{"observed_at":"2026-07-03T20:38:56.170346Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2606.27313","last_updated":"2026-06-30T15:05:33Z","snapshot_observed_at":"2026-07-07T00:01:34.696890Z","submitted_at":"2026-06-25T17:29:27Z","title":"ViQ: Text-Aligned Visual Quantized Representations at Any Resolution","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T05:27:41.578243Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2606.27313"},"observation_digest":"sha256:dc48b7d2c30d158b33c098b47a00e856b3c923efdc2a7798f50f0e3046733178","observation_id":"d7608799-4708-4045-b623-9ca07ce51d91","resolution":{"observed_at":"2026-07-04T13:09:50.849452Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution","version":4},"cited_work":{"arxiv_id":"2409.12961","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.12961","snapshot_observed_at":"2026-07-04T13:09:50.847114Z","title":"Oryx mllm: On- demand spatial-temporal understanding at arbitrary resolution","venue":null,"work_id":"4a905ca2-7426-40db-a509-3452624d3ae5","year":2024},"citing_paper":{"arxiv_id":"2606.27313","last_updated":"2026-06-30T15:05:33Z","snapshot_observed_at":"2026-07-07T00:01:34.696890Z","submitted_at":"2026-06-25T17:29:27Z","title":"ViQ: Text-Aligned Visual Quantized Representations at Any Resolution","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-01T06:29:01.706006Z"},"links":{"cited_paper":"/paper/2409.12961","citing_paper":"/paper/2606.27313"},"observation_digest":"sha256:432715f1e993a291b3ee6feb700eb260efe7d5bbdbc1ef4212829187c6b646ad","observation_id":"1fa8c5de-9e72-4954-ac08-d538123a1901","resolution":{"observed_at":"2026-07-01T09:35:39.749751Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2409.12961/citation-record","integrity":"/paper/2409.12961/integrity","json":"/paper/2409.12961/citation-record.json","paper":"/paper/2409.12961"},"outbound":[],"paper":{"arxiv_id":"2409.12961","last_updated":"2025-02-27T06:09:46Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T19:18:17.523057Z","submitted_at":"2024-09-19T17:59:51Z","title":"Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution"},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 37 inbound Pith citation observations for arXiv:2409.12961."}