{"as_of":"2026-08-16T11:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c989914e00e1d58d0a2252793f44d610b5be76482fd581b4ced5e35cf38ba8c8","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:08:17.224663Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:44:32.150616Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-16T04:44:32.319311Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"cited_work":{"arxiv_id":"2412.12442","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.12442","snapshot_observed_at":"2026-08-16T04:44:32.319311Z","title":"Multi-Task Reinforcement Learning for Quadrotors","venue":"cs.RO","work_id":"8aaad987-41fe-4fd1-8809-1ef7d9f89e33","year":2024},"citing_paper":{"arxiv_id":"2505.00432","last_updated":"2025-05-01T10:01:43Z","snapshot_observed_at":"2026-08-16T04:40:06.125229Z","submitted_at":"2025-05-01T10:01:43Z","title":"A Neural Network Mode for PX4 on Embedded Flight Controllers","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-16T04:44:32.150616Z"},"links":{"cited_paper":"/paper/2412.12442","citing_paper":"/paper/2505.00432"},"observation_digest":"sha256:7e4a57363a93e57f9d1e89a1df9cfc8fc6674be954c82e6892e565350cd04259","observation_id":"9c107462-e2ef-46fc-9bd6-bb7b5e25fc24","resolution":{"observed_at":"2026-08-16T04:44:32.324385Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.12442/citation-record","integrity":"/paper/2412.12442/integrity","json":"/paper/2412.12442/citation-record.json","paper":"/paper/2412.12442"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.715489Z","title":"Reaching the limit in autonomous racing: Optimal control versus reinforcement learning,","venue":null,"work_id":"256e9d54-82f9-4680-b0db-fbcd4d40fab5","year":2023},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.105056Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:22bb8d2bbd77cbd1b2d083ed44a95831cced18ffa922ad0034fe2d4e3888c47f","observation_id":"da1d26ef-ce0f-411f-9210-34562dea3f16","resolution":{"observed_at":"2026-08-11T14:08:17.720083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T14:08:17.110535Z","title":"Autonomous power line inspection with drones via perception-aware mpc,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.110535Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:05cb1554216598ec3f587c947a990a74866c8ffb4fb1224c9a6f74d29a87370e","observation_id":"7d95fde1-3ea1-4e14-bf44-7f2610be5ff2","resolution":{"observed_at":"2026-08-11T14:08:17.110535Z","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":"2026-08-11T14:08:17.690433Z","title":"Con- trastive learning for enhancing robust scene transfer in vision-based agile flight,","venue":null,"work_id":"87d4fe6b-edc7-458f-aa05-da396c2b7814","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.115594Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:7c371cd2c62945318c3d13bc24bd3b37e50ad2dfa7db01d3950eabf48c84edb1","observation_id":"95ca8aa6-ed5e-4661-a06b-abf04bcd0376","resolution":{"observed_at":"2026-08-11T14:08:17.696422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.676308Z","title":"A benchmark comparison of learned control policies for agile quadrotor flight,","venue":null,"work_id":"950a4f5e-e80b-4bfc-9d18-2237ec9c1f64","year":2022},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.120610Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:985a23dba5740a1d8e26de178d2e1c29ee53da9e7931fe733aa492410b6c6195","observation_id":"79a78d5b-ae5f-4491-8d26-fc054bdceaff","resolution":{"observed_at":"2026-08-11T14:08:17.681302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.662400Z","title":"Champion-level drone racing using deep reinforcement learning,","venue":null,"work_id":"a46a9d74-57f7-4f5b-8433-c0b20fbf2a63","year":2023},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.126025Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:a97b82d8f3ab1d7f5fef0e6a5540c96c8e78b6a3bf01dee0e2e954e6ba47e59e","observation_id":"1826d620-dbd9-4c7a-a4e2-bd7baf63752f","resolution":{"observed_at":"2026-08-11T14:08:17.667134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.22308","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.396094Z","title":"Environ- ment as policy: Learning to race in unseen tracks,","venue":null,"work_id":"77ff337e-81ab-4719-8db0-56e59bb95d09","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.131350Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:1ccbf4e903fe47036148f2bf0982fe263c442b3ba3f5e691c7ae17efdd96c5c0","observation_id":"c2c915e0-5399-461e-a367-42b78ab1241b","resolution":{"observed_at":"2026-08-11T14:08:17.402781Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.648802Z","title":"Conflict-averse gradi- ent descent for multi-task learning,","venue":null,"work_id":"58a7f5b1-15d6-477e-8382-5168fa9daafc","year":2021},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.137369Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:772515a8def5d64646f8104b570a71903a15f38f52e2b8a55cd2974161145d0a","observation_id":"3a214b17-9a85-4c52-9d40-5e63ccc19223","resolution":{"observed_at":"2026-08-11T14:08:17.653126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.635148Z","title":"Scaling up multi-task robotic reinforcement learning,","venue":null,"work_id":"473a2623-bdcc-46f8-a6f4-79d83a48f5d5","year":2022},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.141755Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:967138e47ceac6684b05089c981448e12ae7bc4b015f5acbfaa401224eef7aeb","observation_id":"f6b16343-1184-43c1-9e2d-8a3597d3c1a2","resolution":{"observed_at":"2026-08-11T14:08:17.639985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.622437Z","title":"Reset-free reinforcement learning via multi-task learning: Learning dexterous manipulation behaviors without human intervention,","venue":null,"work_id":"2e6fbae8-4f21-4ca9-a537-0d9f6c354294","year":2021},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.146387Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:1746c8a25ad0eaf041bf6a88bfd8c7a087bd5296393e46ea1b676172625197ba","observation_id":"d5cd9d19-a730-4cbb-aa13-aad6f4882f76","resolution":{"observed_at":"2026-08-11T14:08:17.626688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.610643Z","title":"Sharing knowledge in multi-task deep reinforcement learning,","venue":null,"work_id":"c03579d8-2090-4dbd-b70b-9c55acce32f4","year":2020},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.150422Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:8ce72878fc1fe3538195b95dfd988e356e939d4970fc843d998fb95cff391d04","observation_id":"b74d0ff1-21c1-45c9-97d7-72c8cc0b6857","resolution":{"observed_at":"2026-08-11T14:08:17.614694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13466","last_updated":"2024-07-18T12:40:58Z","snapshot_observed_at":"2026-08-13T09:34:55.746950Z","submitted_at":"2024-07-18T12:40:58Z","title":"LIMT: Language-Informed Multi-Task Visual World Models","version":1},"cited_work":{"arxiv_id":"2407.13466","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.13466","snapshot_observed_at":"2026-08-11T14:08:17.270399Z","title":"LIMT: Language-Informed Multi-Task Visual World Models","venue":"cs.RO","work_id":"9c11a848-b6bc-43be-b17c-a337fb1130ef","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.155016Z"},"links":{"cited_paper":"/paper/2407.13466","citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:b2b5438c165e5768ce5254733accf8d7d9daf8887ff4c50cdee4d010b6531817","observation_id":"08abbfcf-e2ea-4d1b-a180-f67797cd9d49","resolution":{"observed_at":"2026-08-11T14:08:17.277053Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.597287Z","title":"Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,","venue":null,"work_id":"5994fa37-9350-4635-a86c-a74077040394","year":2020},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.159813Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:b8d2bef2fe043136c7b30c4e07e21ef715041a3d0acc0ebedfb596485291279e","observation_id":"267b9dfc-8deb-4532-876a-0ce8e4838abb","resolution":{"observed_at":"2026-08-11T14:08:17.602055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T14:08:17.163856Z","title":"Control of a quadrotor with reinforcement learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.163856Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:3609c72a2d689a8560ffa02b4f6970ca13fc3339d647157567fbe232403c37f9","observation_id":"215279d5-1012-49a8-b61b-645728c67d63","resolution":{"observed_at":"2026-08-11T14:08:17.163856Z","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":"2026-08-11T14:08:17.577350Z","title":"Low-level control of a quadrotor with deep model-based reinforcement learning,","venue":null,"work_id":"be7666e6-dd4e-43e8-84f3-135311c6ae35","year":2019},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.167718Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:d0ee4681106057f62893c6c5b700bebe828592bc6e38f714fc7deac744f2363f","observation_id":"2a34fb8a-55d7-4493-8d46-3adab8b1c217","resolution":{"observed_at":"2026-08-11T14:08:17.581819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.565240Z","title":"Learning speed adaptation for flight in clutter,","venue":null,"work_id":"b47fa9e6-5d84-4329-a3b4-e0676f21af88","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.172327Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:088ce5d5c5b20405e71eb6a90613037b6d361c0ef0a4c4566d4657b316d76798","observation_id":"1dd652ce-093f-4db0-b294-eaf1dc47f855","resolution":{"observed_at":"2026-08-11T14:08:17.569163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.553531Z","title":"Collision avoidance and navigation for a quadrotor swarm using end-to-end deep reinforcement learning,","venue":null,"work_id":"2d8b24c8-b02b-4dc3-abdb-c575dfe3b683","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.176215Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:8c60fe99324006995759f5a46bf60d1fb417403fe0c634e1425a7239769157f3","observation_id":"c50ead76-b070-4631-8b48-99fdbabfeb86","resolution":{"observed_at":"2026-08-11T14:08:17.557366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.540624Z","title":"Learning to fly in seconds,","venue":null,"work_id":"88be036c-4ab4-4d1f-affc-e00b0d440d9f","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.180266Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:b646cb9db25c4a39e03c98562785c1956bd584297b61f2cd4045fea465807292","observation_id":"4a3e9511-b59e-4433-bc7a-44ae1daf8d11","resolution":{"observed_at":"2026-08-11T14:08:17.544732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.526757Z","title":"CAD2RL: Real single-image flight without a single real image,","venue":null,"work_id":"50bfeeec-1070-4e10-9154-42470301b5ca","year":2017},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.184022Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:3996d1fe57078229ce0f529202e16fa3f3d3233d74e08dcf316b9139e4f2af07","observation_id":"7434380a-d6a7-4745-8a31-12e73a45a46c","resolution":{"observed_at":"2026-08-11T14:08:17.530978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.512530Z","title":"Bootstrapping reinforcement learning with imitation for vision-based agile flight,","venue":null,"work_id":"0a7599c6-8c33-4784-8c48-bb0d22c974fc","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.187628Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:ed39267d1cd8583867562b4f367f8fff2e0ccb64fece630fca9929de8bbf040c","observation_id":"15ae5ce9-16b8-40f4-a5c6-fad36591b0b8","resolution":{"observed_at":"2026-08-11T14:08:17.517845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.499521Z","title":"Demonstrating agile flight from pixels without state estimation,","venue":null,"work_id":"ca387928-81d7-44f5-8c85-dbdb31d54cf0","year":2024},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.191143Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:7bbda290ce31a600258b7362cbfa11383594fdfe1d6141e6fb1da42e368868c2","observation_id":"754037de-99ff-43ce-88d3-8e056c7b896b","resolution":{"observed_at":"2026-08-11T14:08:17.503393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T14:08:17.194560Z","title":"Multi-task reinforcement learning with soft modularization,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.194560Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:feee373768508b4a8171d9399841ace1efe673799b21a1f67ec87de7c7252bee","observation_id":"45e7bbbf-7178-4f53-9fd6-9fc2109bcdc2","resolution":{"observed_at":"2026-08-11T14:08:17.194560Z","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":"2026-08-11T14:08:17.478592Z","title":"Lifelong robotic reinforcement learning by retain- ing experiences,","venue":null,"work_id":"088f36ae-0166-42a8-a52b-533db2502095","year":2022},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.199083Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:fcabd167012f2cf7fa0da29f0c5d6187182ebdeb6b39c5500e5e07e1a3e12a10","observation_id":"5b81d32c-b932-4939-90ab-5d05a5ddae20","resolution":{"observed_at":"2026-08-11T14:08:17.483392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.464438Z","title":"Autonomous drone racing with deep reinforcement learning,","venue":null,"work_id":"a403eba2-d4af-4fd2-83a4-66e40f197221","year":2021},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.203237Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:b533979d88fff5e2af917fa2099f3e503e0e84142d19fe245dd2d0e7eaa82445","observation_id":"04eb5173-4c69-434b-afbb-9fda6cefe514","resolution":{"observed_at":"2026-08-11T14:08:17.469757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.451649Z","title":"On the continuity of rotation representations in neural networks,","venue":null,"work_id":"d2fb5a8d-4ed7-48f1-b3c5-df3934b69ac6","year":2019},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.207843Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:0c4b154ba4abc71921f0b00c5317104251590fb03dcccd887602ad92a0073188","observation_id":"5e3ce7e2-4293-4d86-ba50-fe913bb9f035","resolution":{"observed_at":"2026-08-11T14:08:17.455995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05990","last_updated":"2020-06-10T17:59:03Z","snapshot_observed_at":"2026-08-14T07:14:28.634639Z","submitted_at":"2020-06-10T17:59:03Z","title":"What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05990","snapshot_observed_at":"2026-08-11T14:08:17.212111Z","title":"What matters in on-policy reinforcement learning? a large-scale empir- ical study,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.212111Z"},"links":{"cited_paper":"/paper/2006.05990","citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:46244bf47565d74bae791b061f85c2df07dc26a3fb9e61d5cf87cbad7aa70767","observation_id":"edfb77bd-b935-4974-906f-0b7e11c672af","resolution":{"observed_at":"2026-08-11T14:08:17.212111Z","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":"2026-08-11T14:08:17.440556Z","title":"Proximal policy optimization algorithms,","venue":null,"work_id":"8f495fc9-59bf-4a38-b9d1-900f34927deb","year":2017},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.216937Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:b287e96d7c946c238e32cf39dd6e5b8d8f331a7c41a29ea2c19a6640c808fd1c","observation_id":"839f3f52-e108-4b19-bdba-5f9e28d66e3a","resolution":{"observed_at":"2026-08-11T14:08:17.444199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.428141Z","title":"Flightmare: A flexible quadrotor simulator,","venue":null,"work_id":"2b9de17b-fdd1-449d-9915-9a7c4dcc30d8","year":2020},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.220816Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:a6e40566fd9329cd906cb5f55b9b6dd6715fe171fd03ecd5f5dc82de09b0e1bd","observation_id":"b5a873ce-9ba4-4d9a-8b55-b8481996b305","resolution":{"observed_at":"2026-08-11T14:08:17.432664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:08:17.413199Z","title":"Agilicious: Open-source and open-hardware agile quadrotor for vision- based flight,","venue":null,"work_id":"c2f64abe-0172-4c3a-ba06-8f0019083b69","year":2022},"citing_paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T14:08:17.224663Z"},"links":{"citing_paper":"/paper/2412.12442"},"observation_digest":"sha256:5a0ba7797128293457649fa2ff0e1e876707f03c7eff8ff58696f1efd112ba17","observation_id":"0b77b8ef-0309-40dd-940e-dd65b19d6189","resolution":{"observed_at":"2026-08-11T14:08:17.418667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.12442","last_updated":"2024-12-17T01:10:18Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-13T09:35:14.337767Z","submitted_at":"2024-12-17T01:10:18Z","title":"Multi-Task Reinforcement Learning for Quadrotors"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":22},"total_outbound_references":28},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2412.12442."}