{"as_of":"2026-08-07T16:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:24c67768394908cf5988884e837a7fc526d0a334b3f90c7676db4a4cd0c01cfd","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:35:13.079539Z","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":"2026-05-17T00:38:44.971183Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.11385","last_updated":"2024-05-05T16:04:52Z","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11385","snapshot_observed_at":"2026-08-07T12:35:13.079539Z","title":"Multi-task rein- forcement learning with mixture of orthogonal experts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24378","last_updated":"2025-05-30T09:08:52Z","snapshot_observed_at":"2026-08-07T12:21:50.691178Z","submitted_at":"2025-05-30T09:08:52Z","title":"Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:13.079539Z"},"links":{"cited_paper":"/paper/2311.11385","citing_paper":"/paper/2505.24378"},"observation_digest":"sha256:79492f3cf36dec7776e63c2b54dd80f6ac5e5deafbb02735d9932648df9e225b","observation_id":"998767c1-e8e4-42ac-935d-36bbe357f3fd","resolution":{"observed_at":"2026-08-07T12:35:13.079539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11385","last_updated":"2024-05-05T16:04:52Z","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11385","snapshot_observed_at":"2026-08-06T11:03:23.515122Z","title":"Multi-task reinforcement learning with mixture of orthogonal experts, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.23172","last_updated":"2025-08-01T01:21:35Z","snapshot_observed_at":"2026-08-06T19:28:36.018078Z","submitted_at":"2025-07-31T00:52:05Z","title":"Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T11:03:23.515122Z"},"links":{"cited_paper":"/paper/2311.11385","citing_paper":"/paper/2507.23172"},"observation_digest":"sha256:b00ebbb151e69b23ff34def3dc6c3b529893f3a618c1cbc857b5093ff8285900","observation_id":"7a321585-e291-49c4-9d6e-32adc78b4397","resolution":{"observed_at":"2026-08-06T11:03:23.515122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11385","last_updated":"2024-05-05T16:04:52Z","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","version":2},"cited_work":{"arxiv_id":"2311.11385","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11385","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"InAdvances in Neural Information Pro- cessing Systems, volume 28, 2944–2952","venue":null,"work_id":"efa01e2d-1160-4df3-a516-3972d2f961e2","year":2023},"citing_paper":{"arxiv_id":"2512.08411","last_updated":"2026-05-13T07:02:09Z","snapshot_observed_at":"2026-08-02T21:03:40.872307Z","submitted_at":"2025-12-09T09:40:34Z","title":"Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-17T00:36:26.202547Z"},"links":{"cited_paper":"/paper/2311.11385","citing_paper":"/paper/2512.08411"},"observation_digest":"sha256:760d24ccf1cc063e880e480889f9680228c9f61a83b7bb044c3d38094487eea6","observation_id":"6c63b9ad-fab1-45d6-b894-60df4a425c10","resolution":{"observed_at":"2026-05-17T00:38:44.974344Z","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":"2311.11385","last_updated":"2024-05-05T16:04:52Z","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","version":2},"cited_work":{"arxiv_id":"2311.11385","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11385","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"InAdvances in Neural Information Pro- cessing Systems, volume 28, 2944–2952","venue":null,"work_id":"efa01e2d-1160-4df3-a516-3972d2f961e2","year":2023},"citing_paper":{"arxiv_id":"2604.03404","last_updated":"2026-04-03T19:05:22Z","snapshot_observed_at":"2026-07-06T22:52:34.609783Z","submitted_at":"2026-04-03T19:05:22Z","title":"Diffusion Policy with Bayesian Expert Selection for Active Multi-Target Tracking","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-13T18:27:25.541838Z"},"links":{"cited_paper":"/paper/2311.11385","citing_paper":"/paper/2604.03404"},"observation_digest":"sha256:974f4e6f58e10c6601726ce02d46935f1c9e300aa8ecf75172cd87a333c28728","observation_id":"dd1c3ec0-4939-4987-be40-97b8ffcb68a6","resolution":{"observed_at":"2026-05-13T18:28:06.410255Z","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":"2311.11385","last_updated":"2024-05-05T16:04:52Z","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","version":2},"cited_work":{"arxiv_id":"2311.11385","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11385","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"InAdvances in Neural Information Pro- cessing Systems, volume 28, 2944–2952","venue":null,"work_id":"efa01e2d-1160-4df3-a516-3972d2f961e2","year":2023},"citing_paper":{"arxiv_id":"2605.09355","last_updated":"2026-05-10T06:09:32Z","snapshot_observed_at":"2026-07-06T23:21:26.017420Z","submitted_at":"2026-05-10T06:09:32Z","title":"FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-12T04:14:04.151375Z"},"links":{"cited_paper":"/paper/2311.11385","citing_paper":"/paper/2605.09355"},"observation_digest":"sha256:ac8cc12f4c09835e5e00566aed3c6d1459e90c840632e3fc28582a0c77db53bc","observation_id":"8dde9a49-2bd8-4e68-9f8a-8a75aeccbca9","resolution":{"observed_at":"2026-05-12T06:31:25.637452Z","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":"2311.11385","last_updated":"2024-05-05T16:04:52Z","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","version":2},"cited_work":{"arxiv_id":"2311.11385","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11385","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"InAdvances in Neural Information Pro- cessing Systems, volume 28, 2944–2952","venue":null,"work_id":"efa01e2d-1160-4df3-a516-3972d2f961e2","year":2023},"citing_paper":{"arxiv_id":"2605.11473","last_updated":"2026-05-12T03:40:28Z","snapshot_observed_at":"2026-08-02T04:53:57.647690Z","submitted_at":"2026-05-12T03:40:28Z","title":"TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-13T01:48:13.679862Z"},"links":{"cited_paper":"/paper/2311.11385","citing_paper":"/paper/2605.11473"},"observation_digest":"sha256:a99d136eafa73787b787d8a434e65da56833ec79519bdf77c64897417308f9e3","observation_id":"f69d0391-fd13-4e3f-9638-b707064873c4","resolution":{"observed_at":"2026-05-13T01:52:05.760292Z","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":"2311.11385","last_updated":"2024-05-05T16:04:52Z","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11385","snapshot_observed_at":"2026-07-31T19:04:33.760614Z","title":"Multi- task reinforcement learning with mixture of orthogonal experts.arXiv preprint arXiv:2311.11385, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24292","last_updated":"2026-07-27T11:39:22Z","snapshot_observed_at":"2026-08-07T12:54:19.753424Z","submitted_at":"2026-07-27T11:39:22Z","title":"Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-31T19:04:33.760614Z"},"links":{"cited_paper":"/paper/2311.11385","citing_paper":"/paper/2607.24292"},"observation_digest":"sha256:ff3a60b77617a9597c26b15d7d313c8f49f75db57ff00097e617f061bab23470","observation_id":"6638c425-8250-4758-bcad-6f9428afe809","resolution":{"observed_at":"2026-07-31T19:04:33.760614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2311.11385/citation-record","integrity":"/paper/2311.11385/integrity","json":"/paper/2311.11385/citation-record.json","paper":"/paper/2311.11385"},"outbound":[],"paper":{"arxiv_id":"2311.11385","last_updated":"2024-05-05T16:04:52Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T16:49:41.895773Z","submitted_at":"2023-11-19T18:09:25Z","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts"},"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":"2026-08-07T06:34:17.273281+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 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2311.11385."}