{"as_of":"2026-08-07T19:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f356acc0ec502cc7ddf6fd04013fa6a03fb4c9bfb52c38c32ab3dc88c7818daf","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:14:07.491267Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T23:03:09.720243Z","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":"2026-07-01T20:56:13.424447Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06167","snapshot_observed_at":"2026-08-05T23:03:09.720243Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.06009","last_updated":"2025-08-08T04:39:16Z","snapshot_observed_at":"2026-08-06T16:03:13.511884Z","submitted_at":"2025-08-08T04:39:16Z","title":"MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-05T23:03:09.720243Z"},"links":{"cited_paper":"/paper/2507.06167","citing_paper":"/paper/2508.06009"},"observation_digest":"sha256:aefdb204d9a82822b0f7193706be8d1cb69a1dc45bcb8c3b48aa31241a8c956d","observation_id":"cbb68054-14f4-434d-bc7c-2dd937822bb7","resolution":{"observed_at":"2026-08-05T23:03:09.720243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"cited_work":{"arxiv_id":"2507.06167","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.06167","snapshot_observed_at":"2026-07-01T20:56:13.424447Z","title":"Skywork-r1v3 technical report","venue":null,"work_id":"98517622-5b2b-43de-a496-2771192df59c","year":2025},"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":110,"source":"pdf_text","source_observed_at":"2026-05-10T11:58:58.660564Z"},"links":{"cited_paper":"/paper/2507.06167","citing_paper":"/paper/2508.18265"},"observation_digest":"sha256:16562381fafa40fff1ab2b68e0892215609754dd47ebce990e699ec89d3074ff","observation_id":"284bbab0-f3fe-422b-86d8-0fa26f273825","resolution":{"observed_at":"2026-05-10T11:58:58.742705Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"cited_work":{"arxiv_id":"2507.06167","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.06167","snapshot_observed_at":"2026-07-01T20:56:13.424447Z","title":"Skywork-r1v3 technical report","venue":null,"work_id":"98517622-5b2b-43de-a496-2771192df59c","year":2025},"citing_paper":{"arxiv_id":"2606.01249","last_updated":"2026-06-17T04:44:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-31T14:04:51Z","title":"Trust Region On-Policy Distillation","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-28T17:38:50.313305Z"},"links":{"cited_paper":"/paper/2507.06167","citing_paper":"/paper/2606.01249"},"observation_digest":"sha256:ac232788c737504934402e233c08782195d3a1362754d71ec4b6db00cc3ff433","observation_id":"b9c262dd-6495-4d59-a469-dcf1e6eae95e","resolution":{"observed_at":"2026-07-01T20:56:13.426115Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"cited_work":{"arxiv_id":"2507.06167","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.06167","snapshot_observed_at":"2026-07-01T20:56:13.424447Z","title":"Skywork-r1v3 technical report","venue":null,"work_id":"98517622-5b2b-43de-a496-2771192df59c","year":2025},"citing_paper":{"arxiv_id":"2606.30217","last_updated":"2026-06-29T12:30:24Z","snapshot_observed_at":"2026-08-03T23:12:01.383471Z","submitted_at":"2026-06-29T12:30:24Z","title":"Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-30T05:55:19.083517Z"},"links":{"cited_paper":"/paper/2507.06167","citing_paper":"/paper/2606.30217"},"observation_digest":"sha256:f54427faded380479aa3fcd2a8998f803db9e5abe8a8167bbc8f5fdb6184b8f9","observation_id":"b8227061-15c5-4a1c-89da-b4b7fb7eb3fb","resolution":{"observed_at":"2026-06-30T13:34:41.128988Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06167","snapshot_observed_at":"2026-07-31T01:31:12.828360Z","title":"arXiv preprint arXiv:2507.06167 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27610","last_updated":"2026-07-30T02:55:02Z","snapshot_observed_at":"2026-08-05T16:42:19.744754Z","submitted_at":"2026-07-30T02:55:02Z","title":"Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-07-31T01:31:12.828360Z"},"links":{"cited_paper":"/paper/2507.06167","citing_paper":"/paper/2607.27610"},"observation_digest":"sha256:bc52579ebc9a2d4509bf342ff5b50542ac9e30f28cf8ec198483dbfa8c170b3f","observation_id":"1923581c-b5b5-43b4-9a48-b9c46c031552","resolution":{"observed_at":"2026-07-31T01:31:12.828360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.06167/citation-record","integrity":"/paper/2507.06167/integrity","json":"/paper/2507.06167/citation-record.json","paper":"/paper/2507.06167"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:10.037795Z","title":"Claude-3.7, 2024","venue":null,"work_id":"55f5c4b0-66d7-4681-ad8c-91325e0adabf","year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:02.709572Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:de84992cc908b80e11591d17a833ca1012cde84a2df332991b48e07d8b7bb6e9","observation_id":"5a21e844-221b-4cb9-a9d1-bda25d4c04e3","resolution":{"observed_at":"2026-08-06T19:14:10.146434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.20330","last_updated":"2024-04-09T15:17:50Z","snapshot_observed_at":"2026-08-07T12:15:30.838846Z","submitted_at":"2024-03-29T17:59:34Z","title":"Are We on the Right Way for Evaluating Large Vision-Language Models?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.20330","snapshot_observed_at":"2026-08-06T19:14:02.779478Z","title":"Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:02.779478Z"},"links":{"cited_paper":"/paper/2403.20330","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:13e089c3aafd58e4d9e87695030696e2ea7bec0a3f31dea519a2004974d8ed1d","observation_id":"4229797c-3c8e-4f03-93be-14741dde1c88","resolution":{"observed_at":"2026-08-06T19:14:02.779478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22617","last_updated":"2025-05-28T17:38:45Z","snapshot_observed_at":"2026-08-07T08:20:32.332400Z","submitted_at":"2025-05-28T17:38:45Z","title":"The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22617","snapshot_observed_at":"2026-08-06T19:14:02.844894Z","title":"The entropy mechanism of reinforcement learning for reasoning language models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:02.844894Z"},"links":{"cited_paper":"/paper/2505.22617","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:b14e9260ebd57a7bc82e3348241ee22abf5e12a60ee1dca086e4ad8e3565fe66","observation_id":"535d1297-7efb-48a0-8c5b-17c0bab84f36","resolution":{"observed_at":"2026-08-06T19:14:02.844894Z","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":"2026-08-06T19:14:09.883080Z","title":"Gemini 2.5: Our most intelligent ai modeld","venue":null,"work_id":"ecbd72ab-dcfa-4c5e-be88-7e901b7bc1aa","year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:02.896171Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:5d2f7f3d657eba942dd5cda294843b631c5938e810e4c5cbf2ae8ad681d2701b","observation_id":"3bf6ea7b-ac95-4af9-bc94-4c9057da9a18","resolution":{"observed_at":"2026-08-06T19:14:09.958077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-06T19:14:02.953006Z","title":"Deepseek-v3 technical report, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:02.953006Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:d57079d240731b07f77dc5a8b8f45882f2845055abe7350c71cd8388d2355e63","observation_id":"b489d9a9-d8f2-4e13-aa6b-f7b7c8aad7bc","resolution":{"observed_at":"2026-08-06T19:14:02.953006Z","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":"2026-08-06T19:14:03.049473Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.049473Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:2156ce77205e5182bd0ea55f8c9b203b02d8e9ba02faf67d2ba4c66590926ce2","observation_id":"e500dfc5-5d10-494b-b80e-c0c4f3624769","resolution":{"observed_at":"2026-08-06T19:14:03.049473Z","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":"2026-08-06T19:14:09.692615Z","title":"Vlmevalkit: An open-source toolkit for evaluating large multi-modality models","venue":null,"work_id":"47739916-154e-4c1a-bde2-69bfd75b8219","year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.119431Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:a073498643a7aec01daac07fd47e3b3454328b266bbd4670db569751c04e18c9","observation_id":"508dd280-b8d1-4d24-a9e5-86cbe04b998c","resolution":{"observed_at":"2026-08-06T19:14:09.747169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14566","last_updated":"2024-03-25T06:05:24Z","snapshot_observed_at":"2026-08-02T21:20:28.501823Z","submitted_at":"2023-10-23T04:49:09Z","title":"HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.14566","snapshot_observed_at":"2026-08-06T19:14:03.176168Z","title":"Hallusionbench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.176168Z"},"links":{"cited_paper":"/paper/2310.14566","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:8122ba9ef4fb123a522aa405faee3f01c5c16d51fd82ece08dd18cb7ea496ef4","observation_id":"3683c845-0d28-4822-aaa6-6c93241a5bb5","resolution":{"observed_at":"2026-08-06T19:14:03.176168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07062","last_updated":"2025-05-11T17:28:30Z","snapshot_observed_at":"2026-08-02T16:13:31.498470Z","submitted_at":"2025-05-11T17:28:30Z","title":"Seed1.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.07062","snapshot_observed_at":"2026-08-06T19:14:03.246738Z","title":"Seed1.5-vl technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.246738Z"},"links":{"cited_paper":"/paper/2505.07062","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:4d99d84fd0ef294d90bdce2edceab661e6b4bdf59384365e2ba4ab4b7585d72e","observation_id":"595aabb9-7d61-48d2-a3f9-3f6748706de8","resolution":{"observed_at":"2026-08-06T19:14:03.246738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.05444","last_updated":"2025-01-09T18:55:52Z","snapshot_observed_at":"2026-08-07T17:40:59.052312Z","submitted_at":"2025-01-09T18:55:52Z","title":"Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.05444","snapshot_observed_at":"2026-08-06T19:14:03.335779Z","title":"Can mllms reason in multimodality? emma: An enhanced multimodal reasoning benchmark","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.335779Z"},"links":{"cited_paper":"/paper/2501.05444","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:1a76a1def302b1b1520acf4acf13fc74a4a0ae3e8a2c4691920ec035bdb6727f","observation_id":"d1c9e39c-da3b-451e-b3c2-7c06538b096d","resolution":{"observed_at":"2026-08-06T19:14:03.335779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.24290","last_updated":"2025-07-05T09:01:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-31T16:36:05Z","title":"Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.24290","snapshot_observed_at":"2026-08-06T19:14:03.414783Z","title":"Open-reasoner-zero: An open source approach to scaling up reinforcement learning on the base model, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.414783Z"},"links":{"cited_paper":"/paper/2503.24290","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:cb403c9812903ef71fcbb22cfea32bde6041888fda857b8e6e2ec390cf4cd9bb","observation_id":"96bd1a85-bc8a-4b40-82ed-4b20e8adedb4","resolution":{"observed_at":"2026-08-06T19:14:03.414783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07987","last_updated":"2024-07-25T09:33:50Z","snapshot_observed_at":"2026-08-06T11:02:06.607024Z","submitted_at":"2024-05-13T17:58:30Z","title":"The Platonic Representation Hypothesis","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07987","snapshot_observed_at":"2026-08-06T19:14:03.502889Z","title":"The platonic representation hypothesis, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.502889Z"},"links":{"cited_paper":"/paper/2405.07987","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:e824222293ff2d497cf1ca577b149bcef440a809f90d02ca7887f684672392b6","observation_id":"cf6ef968-3b80-46ac-9c19-63428e85a8c2","resolution":{"observed_at":"2026-08-06T19:14:03.502889Z","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":"2026-08-06T19:14:03.582847Z","title":"Openai o1 system card","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.582847Z"},"links":{"cited_paper":"/paper/2412.16720","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:384db2e0fbb09e62538369fb8ddf2478d7288da2b2ffd406cb6b59fc7de154bc","observation_id":"3a058f6a-5cb0-4e2e-b170-2e95a9a0cfaf","resolution":{"observed_at":"2026-08-06T19:14:03.582847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24120","last_updated":"2025-06-17T01:56:01Z","snapshot_observed_at":"2026-08-07T12:31:51.195385Z","submitted_at":"2025-05-30T01:34:25Z","title":"CSVQA: A Chinese Multimodal Benchmark for Evaluating STEM Reasoning Capabilities of VLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24120","snapshot_observed_at":"2026-08-06T19:14:03.671614Z","title":"Csvqa: A chinese multimodal benchmark for evaluating stem reasoning capabilities of vlms, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.671614Z"},"links":{"cited_paper":"/paper/2505.24120","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:189d8f70cd196535ca0484616531a391595b47f5212588d0fe09f4a283c25cdc","observation_id":"135949bd-3881-4ccf-9c25-8f809311f889","resolution":{"observed_at":"2026-08-06T19:14:03.671614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08485","last_updated":"2023-12-11T17:46:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-17T17:59:25Z","title":"Visual Instruction Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08485","snapshot_observed_at":"2026-08-06T19:14:03.735189Z","title":"Visual instruction tuning, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.735189Z"},"links":{"cited_paper":"/paper/2304.08485","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:8e3d9a2eb21b2b66c99eab97506bccfef1960bae739d48288f732344a2b43ec0","observation_id":"b2b9d9d1-ea74-4b5f-adb4-cd4893f9b322","resolution":{"observed_at":"2026-08-06T19:14:03.735189Z","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":"2026-08-06T19:14:09.476255Z","title":"Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pp.\\ 216--233","venue":null,"work_id":"a3e6ce8b-a653-40c5-b66e-2ebfd2616f78","year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.823162Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:2a6aa3cc30b4e551fb1b4fed9ef3ab85666e99206eaa82fd1524a25170ce6b9c","observation_id":"dcd0d7d7-88c3-4693-991b-bd6d15889fd1","resolution":{"observed_at":"2026-08-06T19:14:09.600598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02255","last_updated":"2024-01-21T03:47:06Z","snapshot_observed_at":"2026-07-06T16:27:15.027202Z","submitted_at":"2023-10-03T17:57:24Z","title":"MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02255","snapshot_observed_at":"2026-08-06T19:14:03.903007Z","title":"Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.903007Z"},"links":{"cited_paper":"/paper/2310.02255","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:c99dd6cb14483cfe4a3fd94601c0a112fecb1c05c84ce496281c3aa7fd41f909","observation_id":"f058db88-1198-4812-9411-785251fe127b","resolution":{"observed_at":"2026-08-06T19:14:03.903007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07365","last_updated":"2025-04-15T14:22:45Z","snapshot_observed_at":"2026-07-06T20:49:56.018809Z","submitted_at":"2025-03-10T14:23:12Z","title":"MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07365","snapshot_observed_at":"2026-08-06T19:14:03.979993Z","title":"Mm-eureka: Exploring visual aha moment with rule-based large-scale reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:03.979993Z"},"links":{"cited_paper":"/paper/2503.07365","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:d36790aed529722b9c67bd462722b364b258682d22e02eaa415ea0eebb149502","observation_id":"e03ac2b0-2c2f-482c-985a-3c0ecfe4de3e","resolution":{"observed_at":"2026-08-06T19:14:03.979993Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:04.058008Z","title":"Reinforcement learning with verifiable rewards: Grpo's effective loss, dynamics, and success amplification, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.058008Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:66b02b94ab3cc64fd373bf1ec00d57d4716312e07eedaf39ca4b1eea279bbb2b","observation_id":"74189c3c-b628-4905-b979-06d4176af361","resolution":{"observed_at":"2026-08-06T19:14:04.058008Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:04.139055Z","title":"Gui agents: A survey, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.139055Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:f2dde96464f95aba17fd50b2960ff2a1da86f6b4ca72b5491002051d0b61f525","observation_id":"971b8eaf-b210-4bdd-8f78-fd175f7aaa0a","resolution":{"observed_at":"2026-08-06T19:14:04.139055Z","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":"2026-08-06T19:14:09.216099Z","title":"Gpt-4o system card, 2024","venue":null,"work_id":"321d2d7d-16c5-4fa0-96a9-e517e6e65609","year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.221631Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:d5fe703f398beef56ba35b8d85cf565d80675140dba691c348f783d66d608a14","observation_id":"ba4e3c45-f792-423c-bf68-cf264286e13a","resolution":{"observed_at":"2026-08-06T19:14:09.362131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05599","last_updated":"2025-06-09T11:44:18Z","snapshot_observed_at":"2026-08-07T16:07:44.263463Z","submitted_at":"2025-04-08T01:19:20Z","title":"Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05599","snapshot_observed_at":"2026-08-06T19:14:04.302415Z","title":"Skywork r1v: Pioneering multimodal reasoning with chain-of-thought, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.302415Z"},"links":{"cited_paper":"/paper/2504.05599","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:4aec35bb66fb1de031f0c35b2e1a6864188b018957c411c58171cc806692e19b","observation_id":"7ab3dd6f-d702-449b-b23f-dc6d1441d93a","resolution":{"observed_at":"2026-08-06T19:14:04.302415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01284","last_updated":"2024-07-01T13:39:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-01T13:39:08Z","title":"We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01284","snapshot_observed_at":"2026-08-06T19:14:04.409711Z","title":"We-math: Does your large multimodal model achieve human-like mathematical reasoning?, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.409711Z"},"links":{"cited_paper":"/paper/2407.01284","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:8aff1db19c032b8499622520c47d7584286c23a8b274555f583c8b5b7aba593c","observation_id":"3a160e06-b8c6-42d4-b112-c753bc0be00c","resolution":{"observed_at":"2026-08-06T19:14:04.409711Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T19:14:04.527530Z","title":"Proximal policy optimization algorithms, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.527530Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:8bb6a3376eba462117b00a95366a2f79378c08af40a2ebbf33ebe95417661dd0","observation_id":"be0835ab-e60c-4680-8f9e-75ea78db88d9","resolution":{"observed_at":"2026-08-06T19:14:04.527530Z","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":"2026-08-06T19:14:04.599263Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.599263Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:a0e4f42e65a43ffc68a2f39fa04f8acad5320c3624587e8175b146b71552e044","observation_id":"0574a91e-1e55-4d8c-9a75-cec2187d45f5","resolution":{"observed_at":"2026-08-06T19:14:04.599263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.15929","last_updated":"2025-05-29T17:59:14Z","snapshot_observed_at":"2026-08-07T15:08:06.803620Z","submitted_at":"2025-05-21T18:33:50Z","title":"PhyX: Does Your Model Have the \"Wits\" for Physical Reasoning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.15929","snapshot_observed_at":"2026-08-06T19:14:04.701180Z","title":"Phyx: Does your model have the \"wits\" for physical reasoning?, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.701180Z"},"links":{"cited_paper":"/paper/2505.15929","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:e20bd5d2cef240d25738a65bda63147607c9d4b48c8a44c6a8595e01722718e0","observation_id":"741a7c8f-3484-478d-aea1-29d25bd15b1c","resolution":{"observed_at":"2026-08-06T19:14:04.701180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19256","last_updated":"2024-10-02T04:01:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-28T06:20:03Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-08-06T19:14:04.805024Z","title":"Hybridflow: A flexible and efficient rlhf framework","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.805024Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:f4a6db4ad15a2587803fc4c7dc94114d29a5a94678212d1c8522e36c2e38917f","observation_id":"9a89e605-bfa2-4181-9108-5d3ef9e1a824","resolution":{"observed_at":"2026-08-06T19:14:04.805024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.03569","last_updated":"2025-06-04T04:32:54Z","snapshot_observed_at":"2026-08-07T10:57:30.804332Z","submitted_at":"2025-06-04T04:32:54Z","title":"MiMo-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.03569","snapshot_observed_at":"2026-08-06T19:14:04.898698Z","title":"Mimo-vl technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.898698Z"},"links":{"cited_paper":"/paper/2506.03569","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:d3eba6b3b4cfdefb2df6c2b3e9fe70b3d059863d4a97bcb4e01368fb9ad6d8a3","observation_id":"643527ce-67f8-431a-85b6-f4d5b2f11e23","resolution":{"observed_at":"2026-08-06T19:14:04.898698Z","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":"2026-08-06T19:14:09.056384Z","title":"Qvq: To see the world with wisdom","venue":null,"work_id":"8e3dbd26-3a77-4ec9-9149-b6d6a5f1cd0f","year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:04.996610Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:3c29b6d3305f8fdebae574454ab4f67216fb405e88623adc92fb4685a10f09de","observation_id":"da0421c2-987f-488d-90db-8b42b926f40e","resolution":{"observed_at":"2026-08-06T19:14:09.131781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:05.074584Z","title":"Qwq-32b: Embracing the power of reinforcement learning, March 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.074584Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:f1ddc55744cd02951b641d5c3776f0633fe3680adeba7020e8d5dcc2e2b040f5","observation_id":"9d1e3424-ba1e-4a52-9f2a-97a00c5477da","resolution":{"observed_at":"2026-08-06T19:14:05.074584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-06T19:14:05.174677Z","title":"Llama: Open and efficient foundation language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.174677Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:ef3fccab26471a1020b33c07a24a4439ffb17c1654a1dac9b014851c5a98e77f","observation_id":"97929387-2d99-4a4d-a307-fc7ed4238abd","resolution":{"observed_at":"2026-08-06T19:14:05.174677Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:05.279105Z","title":"Vlm see, robot do: Human demo video to robot action plan via vision language model, 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.279105Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:fe31e57eaf5690584258d78ff4d1d2f4bed8cfc6f8b47b37a2bd2ab8a6bf8954","observation_id":"3c6b83af-f93f-4b88-bc2d-e6de4a6c3ec2","resolution":{"observed_at":"2026-08-06T19:14:05.279105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08343","last_updated":"2025-06-18T14:43:36Z","snapshot_observed_at":"2026-08-07T08:58:45.702180Z","submitted_at":"2025-06-10T01:54:04Z","title":"Wait, We Don't Need to \"Wait\"! Removing Thinking Tokens Improves Reasoning Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.08343","snapshot_observed_at":"2026-08-06T19:14:05.383320Z","title":"Wait, we don't need to \"wait\"! removing thinking tokens improves reasoning efficiency, 2025 a","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.383320Z"},"links":{"cited_paper":"/paper/2506.08343","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:c45749fea7136620b62b64e59cbca0dc0d0f5d613acfd406caf81b179e4f51cb","observation_id":"9a4d746d-7b67-4691-91f0-b60bb38d31a4","resolution":{"observed_at":"2026-08-06T19:14:05.383320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16656","last_updated":"2025-06-06T07:27:18Z","snapshot_observed_at":"2026-08-07T15:59:55.050120Z","submitted_at":"2025-04-23T12:24:10Z","title":"Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16656","snapshot_observed_at":"2026-08-06T19:14:05.468540Z","title":"Skywork r1v2: Multimodal hybrid reinforcement learning for reasoning, 2025 b","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.468540Z"},"links":{"cited_paper":"/paper/2504.16656","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:1834388e7032c3ed27472f349a79d17d9d59d2408e1d7706007161082f692ab6","observation_id":"37c69bd4-167e-4749-ae2e-08166d75dcbc","resolution":{"observed_at":"2026-08-06T19:14:05.468540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12191","last_updated":"2024-10-03T15:54:49Z","snapshot_observed_at":"2026-08-06T05:35:29.109022Z","submitted_at":"2024-09-18T17:59:32Z","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12191","snapshot_observed_at":"2026-08-06T19:14:05.557073Z","title":"Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.557073Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:f0e18fc9364428feed7794b2f799ac465801a99aa5c7733081c46ba74d559959","observation_id":"8679045a-0f04-4cf2-aedc-c27c53c0b850","resolution":{"observed_at":"2026-08-06T19:14:05.557073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.01939","last_updated":"2025-11-13T10:08:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-02T17:54:39Z","title":"Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.01939","snapshot_observed_at":"2026-08-06T19:14:05.659753Z","title":"Beyond the 80/20 rule: High-entropy minority tokens drive effective reinforcement learning for llm reasoning, 2025 c","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.659753Z"},"links":{"cited_paper":"/paper/2506.01939","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:2ba80a69673e54b1676d01a94702520fc0b9b133475bfd6978f44183c6f73b43","observation_id":"a4547d75-1c6b-4cc3-8802-a3e166456278","resolution":{"observed_at":"2026-08-06T19:14:05.659753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07263","last_updated":"2025-06-09T11:46:47Z","snapshot_observed_at":"2026-08-07T15:47:58.962808Z","submitted_at":"2025-05-12T06:23:08Z","title":"Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.07263","snapshot_observed_at":"2026-08-06T19:14:05.742695Z","title":"Skywork-vl reward: An effective reward model for multimodal understanding and reasoning, 2025 d","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.742695Z"},"links":{"cited_paper":"/paper/2505.07263","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:a99c40d075a8a211ecf0f558423ff1f695b41793175d876f0b6fde010e05d8fe","observation_id":"daea2ec7-3631-4e7d-a0ea-a91d883a4ca9","resolution":{"observed_at":"2026-08-06T19:14:05.742695Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:05.852966Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.852966Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:f5b0d0cbce762dd47a05f49d21c48ab2b1802f363a8c5c2a1f60a8f0c790f55c","observation_id":"aee4c84e-8613-4ebe-bedf-9de25d4815eb","resolution":{"observed_at":"2026-08-06T19:14:05.852966Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:05.976737Z","title":"Seephys: Does seeing help thinking? -- benchmarking vision-based physics reasoning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:05.976737Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:d6ef1d1442b8fbfedea26462a6adba7b205afa457e405eec051b0a4c45cb7561","observation_id":"fe375c0f-94e8-4dea-86cf-6a60774b62e9","resolution":{"observed_at":"2026-08-06T19:14:05.976737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04973","last_updated":"2024-07-06T06:48:16Z","snapshot_observed_at":"2026-07-06T18:42:18.289966Z","submitted_at":"2024-07-06T06:48:16Z","title":"LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04973","snapshot_observed_at":"2026-08-06T19:14:06.071640Z","title":"Logicvista: Multimodal llm logical reasoning benchmark in visual contexts, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.071640Z"},"links":{"cited_paper":"/paper/2407.04973","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:0419bf086d4a89c1fd1de680a424b78dcf2c33b95b7996619887ca6576442693","observation_id":"0924ea39-aaa8-4179-bbb6-7b694a9fc070","resolution":{"observed_at":"2026-08-06T19:14:06.071640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15279","last_updated":"2025-04-21T17:59:53Z","snapshot_observed_at":"2026-08-07T16:00:29.784743Z","submitted_at":"2025-04-21T17:59:53Z","title":"VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15279","snapshot_observed_at":"2026-08-06T19:14:06.185506Z","title":"Visulogic: A benchmark for evaluating visual reasoning in multi-modal large language models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.185506Z"},"links":{"cited_paper":"/paper/2504.15279","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:75dad5580ca68724852bf227cc344c5c8ca0d883573c2f30560fcf4ae96ea438","observation_id":"6c08403c-c187-468a-aa4f-e2cdd37cd97f","resolution":{"observed_at":"2026-08-06T19:14:06.185506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.14446","last_updated":"2025-08-29T20:50:08Z","snapshot_observed_at":"2026-08-02T03:23:17.398540Z","submitted_at":"2024-12-19T01:53:36Z","title":"VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.14446","snapshot_observed_at":"2026-08-06T19:14:06.267417Z","title":"Meyer, Siva Karthik Mustikovela, Siddhartha Srinivasa, Eric M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.267417Z"},"links":{"cited_paper":"/paper/2412.14446","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:ba0d026c2ae6943275a16b446a6226c230adf38b663e3614b7c267665be599e6","observation_id":"6fd8a4a4-b6bd-4978-b53e-6577ef55547c","resolution":{"observed_at":"2026-08-06T19:14:06.267417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T19:14:06.381013Z","title":"Dapo: An open-source llm reinforcement learning system at scale, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.381013Z"},"links":{"cited_paper":"/paper/2503.14476","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:80bb2883aa2436e529f4e7f457a155429d96c24cb624879512eaedad378fd0cd","observation_id":"b5f6112d-1b2a-4d81-a4c2-9bf9568cbb3e","resolution":{"observed_at":"2026-08-06T19:14:06.381013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.21327","last_updated":"2025-05-27T15:23:23Z","snapshot_observed_at":"2026-08-07T13:27:31.159147Z","submitted_at":"2025-05-27T15:23:23Z","title":"MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.21327","snapshot_observed_at":"2026-08-06T19:14:06.477729Z","title":"Mme-reasoning: A comprehensive benchmark for logical reasoning in mllms, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.477729Z"},"links":{"cited_paper":"/paper/2505.21327","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:44535ffe0c4c65849976c0259a4f42fba7c56a2ffecc6d2b5902c2fecc2560b7","observation_id":"f87cc9b9-efdc-4bf8-a416-047cb2e25476","resolution":{"observed_at":"2026-08-06T19:14:06.477729Z","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":"2026-08-06T19:14:08.816934Z","title":"Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi","venue":null,"work_id":"d9497de7-16bf-4221-8812-82028320d887","year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.625970Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:5b511741b3b64fb3547d14894526e2f9cd091801d5c5a6ab33933c711147acc5","observation_id":"2a32af9c-5af0-423b-a85c-82427b31b436","resolution":{"observed_at":"2026-08-06T19:14:08.925592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.02813","last_updated":"2025-05-22T08:22:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-04T15:31:26Z","title":"MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.02813","snapshot_observed_at":"2026-08-06T19:14:06.726951Z","title":"Mmmu-pro: A more robust multi-discipline multimodal understanding benchmark, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.726951Z"},"links":{"cited_paper":"/paper/2409.02813","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:ebcf774ed7ba4d0770d3a3f67dd9a3c5243a096b0586929158ad124836086434","observation_id":"1e3744ac-1aff-4e6d-85fb-6b72bfc1dc7c","resolution":{"observed_at":"2026-08-06T19:14:06.726951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23043","last_updated":"2025-05-29T03:40:21Z","snapshot_observed_at":"2026-08-07T12:52:49.331015Z","submitted_at":"2025-05-29T03:40:21Z","title":"Are Unified Vision-Language Models Necessary: Generalization Across Understanding and Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23043","snapshot_observed_at":"2026-08-06T19:14:06.879405Z","title":"Are unified vision-language models necessary: Generalization across understanding and generation, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.879405Z"},"links":{"cited_paper":"/paper/2505.23043","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:8f45522e32d49a6e5a070edc53f9476fcb61c9beae502e9cc35554c654bf15fc","observation_id":"bfbb7e93-76ba-4638-918e-68d65e3803b1","resolution":{"observed_at":"2026-08-06T19:14:06.879405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14624","last_updated":"2024-08-18T08:10:16Z","snapshot_observed_at":"2026-08-04T04:40:00.850270Z","submitted_at":"2024-03-21T17:59:50Z","title":"MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14624","snapshot_observed_at":"2026-08-06T19:14:06.985583Z","title":"Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:06.985583Z"},"links":{"cited_paper":"/paper/2403.14624","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:c1945534b865bb5092316ae8b0b754a8000839b799d33768846bce4235355a35","observation_id":"18c26c28-4317-45f4-8223-12cb371da5aa","resolution":{"observed_at":"2026-08-06T19:14:06.985583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.19839","last_updated":"2025-03-29T15:38:25Z","snapshot_observed_at":"2026-08-07T16:37:48.196581Z","submitted_at":"2025-03-25T16:59:42Z","title":"FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.19839","snapshot_observed_at":"2026-08-06T19:14:07.085583Z","title":"Fireedit: Fine-grained instruction-based image editing via region-aware vision language model, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:07.085583Z"},"links":{"cited_paper":"/paper/2503.19839","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:f3d4b4e31a603d806164e052ac68e8706b854c69c68cf46bff5379a39e48c435","observation_id":"4ed4e24d-b714-4ca2-bf3f-38a2375581d8","resolution":{"observed_at":"2026-08-06T19:14:07.085583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_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},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.10479","snapshot_observed_at":"2026-08-06T19:14:07.140185Z","title":"Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:07.140185Z"},"links":{"cited_paper":"/paper/2504.10479","citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:79898a9c1ae7f8757e0a9f2d8d7ac796d61436ce31fedb2cd3d76c29556cbb1e","observation_id":"12c77fae-44e4-4e2f-8811-b35043c4d6db","resolution":{"observed_at":"2026-08-06T19:14:07.140185Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:07.246466Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:07.246466Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:45dce49cb30e9ec56be9728eff8173c35bd0f97dedb57e8395b013d3b7f27856","observation_id":"7760a4b9-55c7-421c-add5-6b21c27bc001","resolution":{"observed_at":"2026-08-06T19:14:07.246466Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:07.315396Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:07.315396Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:60ea865477f474fc1a2868e1e62aef85a179e2a94c7bcad491af56f2e4cdd831","observation_id":"d627a986-424d-4493-89c7-0b61ee0addb6","resolution":{"observed_at":"2026-08-06T19:14:07.315396Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:07.421669Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:07.421669Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:221b021036410aa09beeea147a529bad46124be62e2d65c443800ab3a1a596b0","observation_id":"7aaa3473-6c5a-47f6-9358-2e0a43cea8ee","resolution":{"observed_at":"2026-08-06T19:14:07.421669Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:14:07.491267Z","title":"当她在你的个人笔记本电脑里看到一个名为“models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:07.491267Z"},"links":{"citing_paper":"/paper/2507.06167"},"observation_digest":"sha256:216eae5bbf6203891c64423f32735883aa9638fccb23714cd33c24d96a555906","observation_id":"943bc98f-5c76-46f3-a89f-8b7ffc65d0a1","resolution":{"observed_at":"2026-08-06T19:14:07.491267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.06167","last_updated":"2025-07-10T15:41:04Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T10:37:03.707334Z","submitted_at":"2025-07-08T16:47:16Z","title":"Skywork-R1V3 Technical Report"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":47,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":54},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 5 inbound Pith citation observations for arXiv:2507.06167."}