{"as_of":"2026-08-22T12:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e4799943e4d6d29e0c22f9cbf00bece451c73b81631c20be94ba810f9d7d4b19","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T10:13:40.580329Z","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":"2026-05-25T02:40:14.746588Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.06513","last_updated":"2024-10-09T03:27:14Z","snapshot_observed_at":"2026-08-16T13:11:17.771981Z","submitted_at":"2024-10-09T03:27:14Z","title":"MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06513","snapshot_observed_at":"2026-08-12T10:13:40.580329Z","title":"Motionrl: Align text-to-motion generation to human preferences with multi-reward reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19459","last_updated":"2024-11-29T04:09:13Z","snapshot_observed_at":"2026-08-16T16:36:55.743106Z","submitted_at":"2024-11-29T04:09:13Z","title":"Fleximo: Towards Flexible Text-to-Human Motion Video Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T10:13:40.580329Z"},"links":{"cited_paper":"/paper/2410.06513","citing_paper":"/paper/2411.19459"},"observation_digest":"sha256:cabed643bad37fa9cdaf882533a0ff6049be068bec6c62dd07b7165716d77812","observation_id":"635a1e0d-0bcc-49a2-9b29-6856134bbbd9","resolution":{"observed_at":"2026-08-12T10:13:40.580329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06513","last_updated":"2024-10-09T03:27:14Z","snapshot_observed_at":"2026-08-16T13:11:17.771981Z","submitted_at":"2024-10-09T03:27:14Z","title":"MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06513","snapshot_observed_at":"2026-08-03T17:06:56.393617Z","title":"Motionrl: Align text-to-motion generation to human preferences with multi-reward reinforcement learning.arXiv preprint arXiv:2410.06513, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.10730","last_updated":"2026-07-04T09:34:06Z","snapshot_observed_at":"2026-08-21T11:34:51.111103Z","submitted_at":"2025-12-11T15:16:06Z","title":"IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T17:06:56.393617Z"},"links":{"cited_paper":"/paper/2410.06513","citing_paper":"/paper/2512.10730"},"observation_digest":"sha256:ebae6877f0bc780dfba4bd9ea333e62df69ecd0d9760b7184a78fb321a37ab2d","observation_id":"d56ceb1a-d05e-4cb3-99b8-a1149bfa99d5","resolution":{"observed_at":"2026-08-03T17:06:56.393617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06513","last_updated":"2024-10-09T03:27:14Z","snapshot_observed_at":"2026-08-16T13:11:17.771981Z","submitted_at":"2024-10-09T03:27:14Z","title":"MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2410.06513","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.06513","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2410.06513 , year=","venue":null,"work_id":"168a3037-44ee-4306-93af-6d468159c68a","year":2024},"citing_paper":{"arxiv_id":"2605.18956","last_updated":"2026-05-18T18:00:04Z","snapshot_observed_at":"2026-08-14T23:56:08.232475Z","submitted_at":"2026-05-18T18:00:04Z","title":"MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-20T10:37:25.892898Z"},"links":{"cited_paper":"/paper/2410.06513","citing_paper":"/paper/2605.18956"},"observation_digest":"sha256:c65f4a8e662a150d1547a503f67138e184d3a80b9277226826e72fbca0ab3724","observation_id":"0f1497c4-cead-4477-bb3f-36d54360a337","resolution":{"observed_at":"2026-05-20T10:38:12.356758Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06513","last_updated":"2024-10-09T03:27:14Z","snapshot_observed_at":"2026-08-16T13:11:17.771981Z","submitted_at":"2024-10-09T03:27:14Z","title":"MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2410.06513","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.06513","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2410.06513 , year=","venue":null,"work_id":"168a3037-44ee-4306-93af-6d468159c68a","year":2024},"citing_paper":{"arxiv_id":"2605.22894","last_updated":"2026-05-25T05:33:57Z","snapshot_observed_at":"2026-08-18T23:21:17.384776Z","submitted_at":"2026-05-21T14:17:21Z","title":"SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-05-25T02:38:14.542361Z"},"links":{"cited_paper":"/paper/2410.06513","citing_paper":"/paper/2605.22894"},"observation_digest":"sha256:f89ca574f5bb1bf0dd74a1d39ed56899b21c3aad1bb4b0bfabe6617f4164636a","observation_id":"c809c625-af8b-4bd2-b532-e536442f504a","resolution":{"observed_at":"2026-05-25T02:40:14.749926Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.06513/citation-record","integrity":"/paper/2410.06513/integrity","json":"/paper/2410.06513/citation-record.json","paper":"/paper/2410.06513"},"outbound":[],"paper":{"arxiv_id":"2410.06513","last_updated":"2024-10-09T03:27:14Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T13:11:17.771981Z","submitted_at":"2024-10-09T03:27:14Z","title":"MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.06513."}