{"as_of":"2026-08-10T08:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32f2525e911a9e5b83d9509ee20cf70abd30bc377d57566b935bbf51b4deccb3","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:18:39.317873Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T07:20:54.642591Z","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":"2026-06-30T07:24:21.251853Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2506.21980","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21980","snapshot_observed_at":"2026-06-30T07:24:21.251853Z","title":"R1-track: Direct application of mllms to visual object tracking via reinforcement learning","venue":null,"work_id":"603a5ed6-b789-43ec-a241-77b44c0ff954","year":2025},"citing_paper":{"arxiv_id":"2512.03043","last_updated":"2026-04-28T12:07:36Z","snapshot_observed_at":"2026-08-02T09:59:18.546374Z","submitted_at":"2025-12-02T18:59:52Z","title":"OneThinker: All-in-one Reasoning Model for Image and Video","version":3},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-17T02:09:39.820651Z"},"links":{"cited_paper":"/paper/2506.21980","citing_paper":"/paper/2512.03043"},"observation_digest":"sha256:61ff2823fcc4cd54a1c011b624b59fe7214ff376ba3a549fc86ba120e1bfd359","observation_id":"e309a90c-410c-462d-abc7-833fe7e97128","resolution":{"observed_at":"2026-05-17T02:11:26.591440Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2506.21980","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21980","snapshot_observed_at":"2026-06-30T07:24:21.251853Z","title":"R1-track: Direct application of mllms to visual object tracking via reinforcement learning","venue":null,"work_id":"603a5ed6-b789-43ec-a241-77b44c0ff954","year":2025},"citing_paper":{"arxiv_id":"2512.20260","last_updated":"2026-05-01T10:57:55Z","snapshot_observed_at":"2026-07-06T22:39:52.837578Z","submitted_at":"2025-12-23T11:16:16Z","title":"Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations","version":5},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-16T20:12:33.250097Z"},"links":{"cited_paper":"/paper/2506.21980","citing_paper":"/paper/2512.20260"},"observation_digest":"sha256:d5605d943cf1a63a754fa7675e93ce80eed0a2d97c38cc98aa9cb4542d2ed438","observation_id":"d8e3894d-1631-41d6-9596-19a46a6c472c","resolution":{"observed_at":"2026-05-16T20:13:22.984143Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2506.21980","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21980","snapshot_observed_at":"2026-06-30T07:24:21.251853Z","title":"R1-track: Direct application of mllms to visual object tracking via reinforcement learning","venue":null,"work_id":"603a5ed6-b789-43ec-a241-77b44c0ff954","year":2025},"citing_paper":{"arxiv_id":"2604.08014","last_updated":"2026-04-21T06:24:07Z","snapshot_observed_at":"2026-08-02T00:17:26.235002Z","submitted_at":"2026-04-09T09:14:00Z","title":"Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T18:38:16.204012Z"},"links":{"cited_paper":"/paper/2506.21980","citing_paper":"/paper/2604.08014"},"observation_digest":"sha256:f43c672048843bf1a10acf5b457033066c8f986c94a1697e12238b1922e16484","observation_id":"089fe88c-1ecb-499f-81a2-338d51b865be","resolution":{"observed_at":"2026-05-11T00:15:52.937357Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2506.21980","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21980","snapshot_observed_at":"2026-06-30T07:24:21.251853Z","title":"R1-track: Direct application of mllms to visual object tracking via reinforcement learning","venue":null,"work_id":"603a5ed6-b789-43ec-a241-77b44c0ff954","year":2025},"citing_paper":{"arxiv_id":"2604.17898","last_updated":"2026-04-20T07:17:59Z","snapshot_observed_at":"2026-08-02T16:44:23.989836Z","submitted_at":"2026-04-20T07:17:59Z","title":"ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-10T04:41:23.980996Z"},"links":{"cited_paper":"/paper/2506.21980","citing_paper":"/paper/2604.17898"},"observation_digest":"sha256:9d68c88fbea6aab0e0dc319f8b368c7810adf392993b742c0c861535be11be88","observation_id":"395c07ae-93c3-4105-8e31-77d8e6e840dd","resolution":{"observed_at":"2026-05-10T12:05:23.050130Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2506.21980","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21980","snapshot_observed_at":"2026-06-30T07:24:21.251853Z","title":"R1-track: Direct application of mllms to visual object tracking via reinforcement learning","venue":null,"work_id":"603a5ed6-b789-43ec-a241-77b44c0ff954","year":2025},"citing_paper":{"arxiv_id":"2604.18051","last_updated":"2026-04-20T10:19:07Z","snapshot_observed_at":"2026-08-03T00:54:49.645105Z","submitted_at":"2026-04-20T10:19:07Z","title":"INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T04:41:57.207279Z"},"links":{"cited_paper":"/paper/2506.21980","citing_paper":"/paper/2604.18051"},"observation_digest":"sha256:9dae577e3fec40cea85581042ea2378e76de00fac9a497573a0c7a98bb1d1df9","observation_id":"e7348589-ec91-488b-b6a9-17f16c6d17a8","resolution":{"observed_at":"2026-05-10T12:05:22.673159Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2506.21980","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21980","snapshot_observed_at":"2026-06-30T07:24:21.251853Z","title":"R1-track: Direct application of mllms to visual object tracking via reinforcement learning","venue":null,"work_id":"603a5ed6-b789-43ec-a241-77b44c0ff954","year":2025},"citing_paper":{"arxiv_id":"2606.29357","last_updated":"2026-06-28T12:12:18Z","snapshot_observed_at":"2026-07-07T00:03:21.717191Z","submitted_at":"2026-06-28T12:12:18Z","title":"Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-30T07:20:54.642591Z"},"links":{"cited_paper":"/paper/2506.21980","citing_paper":"/paper/2606.29357"},"observation_digest":"sha256:093e7e2dca0308d3e15fe644b78437eb07ae229e55d1e92444f60fe8dc29a8de","observation_id":"237d682c-c674-4c7a-919d-5a3f979fd039","resolution":{"observed_at":"2026-06-30T07:24:21.253447Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.21980/citation-record","integrity":"/paper/2506.21980/integrity","json":"/paper/2506.21980/citation-record.json","paper":"/paper/2506.21980"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:42.602422Z","title":"High-Speed Tracking with Kernelized Correlation Filters,","venue":null,"work_id":"cb9aa927-b1a2-40fd-a1cb-6ae09105f68a","year":2015},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:36.490583Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:013b3cba8660f091b6896eff1eb8687399be6c3d38df6c3f63f3a45a2b44ca52","observation_id":"6823e212-c87a-4de3-a1f1-22e76dfe2bc0","resolution":{"observed_at":"2026-08-06T22:18:42.675512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:42.343306Z","title":"ECO: Efficient convolution operators for tracking,","venue":null,"work_id":"ccfaa1a3-a4c6-4dbd-80a5-cadb7254063f","year":2017},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:36.750988Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:2dcd763fbea65dae8762217b2c11865a5af06eabbae37f0b3562f1ca577039ff","observation_id":"c45c0873-9cbc-4a79-b63b-aaf71d954722","resolution":{"observed_at":"2026-08-06T22:18:42.453711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:42.141228Z","title":"SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks,","venue":null,"work_id":"ad162691-da8e-4944-b90b-326021b1666e","year":2019},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:36.835779Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:e546993a56c305719a55ef42ed71d119570eef982b30e777ca5f98cdbb487d5a","observation_id":"57d47af3-446f-49c6-877c-a632b49e44f0","resolution":{"observed_at":"2026-08-06T22:18:42.237028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:41.788443Z","title":"Transformer Tracking","venue":null,"work_id":"d54f064a-9176-4378-90c2-b748864d0bdf","year":2021},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:36.960457Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:97ef69606a7dd2a2044eb3d03009e18c5697e82bf07ca79249ee726cfa79bf7f","observation_id":"53265930-aa51-481e-bc98-81a3ed6b4324","resolution":{"observed_at":"2026-08-06T22:18:41.943251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:41.533539Z","title":"Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework","venue":null,"work_id":"c11c44f3-a995-4056-b0cd-f9dcb4921e5c","year":2022},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:37.142351Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:d56d27c414752cc93beca520624a08f4946a08c058a0f3e43c6959c1bebdc9e0","observation_id":"1b4712eb-771c-4ffd-bc9e-c3a3335a0d67","resolution":{"observed_at":"2026-08-06T22:18:41.631119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:41.270237Z","title":"Language models are few-shot learners","venue":null,"work_id":"49f4d3c8-90ee-4c30-b411-7f6ca05508d3","year":2020},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:37.275271Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:ca0a44374e17352cb9396d7e4b614dfb539546a5b63869cb6ca1578e7ad928c7","observation_id":"83ac570b-33bb-4a2e-accf-8dd2e9a0d6cc","resolution":{"observed_at":"2026-08-06T22:18:41.386405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:41.035732Z","title":"Visual instruction tuning","venue":null,"work_id":"682ce8a7-6d5b-448b-92e1-7b3a8ac08b4c","year":2023},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:37.494877Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:2747d8d86277e222609141afcb3481ea5b55421529ecb9d1e11c6c16f44d5b8e","observation_id":"cd98c7ac-9640-493c-81aa-5218806e30aa","resolution":{"observed_at":"2026-08-06T22:18:41.138987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.10479","last_updated":"2025-04-19T03:47:21Z","snapshot_observed_at":"2026-08-10T07:07:56.707005Z","submitted_at":"2025-04-14T17:59:25Z","title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.10479","snapshot_observed_at":"2026-08-06T22:18:37.640165Z","title":"Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:37.640165Z"},"links":{"cited_paper":"/paper/2504.10479","citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:95d04907ab4558db85f2b82cbed0f93bb3fb08a5840230a4c73ef3571a342053","observation_id":"853d58cd-65fb-4489-8823-a2d88a1469ae","resolution":{"observed_at":"2026-08-06T22:18:37.640165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-08-06T22:18:37.774926Z","title":"Qwen2. 5-vl technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:37.774926Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:7265975982619f5334bda11d11e3a916461033232ee52da01cb0fbbf80d6f807","observation_id":"875ab6fc-f989-4412-b70f-77e93a694ec5","resolution":{"observed_at":"2026-08-06T22:18:37.774926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16720","last_updated":"2026-04-30T02:46:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-21T18:04:31Z","title":"OpenAI o1 System Card","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16720","snapshot_observed_at":"2026-08-06T22:18:37.903290Z","title":"Openai o1 system card","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:37.903290Z"},"links":{"cited_paper":"/paper/2412.16720","citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:0379df3c3940ee285c0cab28a5bb0e5b59f75e1ef09ccc92b1ec15b996d2e462","observation_id":"864aedb7-025c-4cb2-bf8c-257e8fb891f5","resolution":{"observed_at":"2026-08-06T22:18:37.903290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-06T22:18:37.995094Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:37.995094Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:48b6dedb4aaf899223d8f1cd22e53c729d4f771f633960c87e4a0079ca7667c8","observation_id":"7c77dbd7-ed54-4eed-8daa-5368d9bd899a","resolution":{"observed_at":"2026-08-06T22:18:37.995094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-06T22:18:38.262307Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:38.262307Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:f7d7078e26b7890b5e5a767249f6aa408e5e4d81ca801dd0f7ac87a34e62b421","observation_id":"04e945a1-948e-4042-a7cb-185f16c8ab5e","resolution":{"observed_at":"2026-08-06T22:18:38.262307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:40.723030Z","title":"Got-10k: A large high-diversity benchmark for generic object tracking in the wild","venue":null,"work_id":"026f4dc7-4bd2-4003-b67c-f3002f568671","year":2019},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:38.425406Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:22989057689df06b3995e81bedd0e7976da1cd9ae40811dd27c7193cf2dd2e99","observation_id":"8f16a149-0c7f-4882-863e-7d2d678dfb47","resolution":{"observed_at":"2026-08-06T22:18:40.871319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-06T22:18:38.554959Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:38.554959Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:bbf8b7fd328d3852282f5b0ed6c29493947d067eb8569f4e1c941ad17257f80d","observation_id":"4996425c-bdbd-46de-a7c4-a1825e9501f7","resolution":{"observed_at":"2026-08-06T22:18:38.554959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:40.429692Z","title":"EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework","venue":null,"work_id":"140cd2fd-999b-4bf3-8d70-e8ff5053dd0a","year":2025},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:38.727314Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:ba97926f088907344244c489efa71f04951b11aaf39d3642614b3f19b83b7e12","observation_id":"ad34a03a-5146-4ddc-99b5-6d510bcd0bee","resolution":{"observed_at":"2026-08-06T22:18:40.619093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14476","last_updated":"2025-05-20T01:37:34Z","snapshot_observed_at":"2026-08-02T01:40:54.187278Z","submitted_at":"2025-03-18T17:49:06Z","title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14476","snapshot_observed_at":"2026-08-06T22:18:38.866409Z","title":"Dapo: An open-source llm reinforcement learning system at scale","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:38.866409Z"},"links":{"cited_paper":"/paper/2503.14476","citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:72f03b0c5cf3c66c14897f125452405a3a47ead90dca3a8077f6367ca6c23a31","observation_id":"59345c50-d6df-423d-8fc6-9aacd2ec5339","resolution":{"observed_at":"2026-08-06T22:18:38.866409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:40.101788Z","title":"Generalized intersection over union: A metric and a loss for bounding box regression","venue":null,"work_id":"62b9a7a5-e904-4bbc-b35b-b5584918e674","year":2019},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:38.983117Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:cd88f8493bd4a37017cd3decaaff819d1ebdbf6f252bc1bfd4e91f301d3c44aa","observation_id":"7db56ba5-61e6-441f-9f34-411054e36e64","resolution":{"observed_at":"2026-08-06T22:18:40.235389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:39.830744Z","title":"Efficient memory management for large language model serving with pagedattention","venue":null,"work_id":"52fae459-06e6-4afb-89b1-6faf23e6fc4a","year":2023},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:39.117650Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:f05a83fd7df245051db00c91cf48577881a3c3d4e8770d2aa6c8d67adca976ce","observation_id":"d1baa265-8dab-40a6-b45b-718a038eceb5","resolution":{"observed_at":"2026-08-06T22:18:39.955767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:18:39.560825Z","title":"Improved baselines with visual instruction tuning","venue":null,"work_id":"5359ccdd-f1c6-4138-99f5-178b33f679d2","year":2024},"citing_paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:39.317873Z"},"links":{"citing_paper":"/paper/2506.21980"},"observation_digest":"sha256:e57e5f07d4612f67abc19cea69f6240c93e26d194d3f1aa6f41e9285bcf1faab","observation_id":"296d11de-f1c0-4e9c-a13a-759223b5921d","resolution":{"observed_at":"2026-08-06T22:18:39.675939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.21980","last_updated":"2025-07-22T15:39:40Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T22:11:53.372622Z","submitted_at":"2025-06-27T07:41:15Z","title":"R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":19},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 6 inbound Pith citation observations for arXiv:2506.21980."}