{"as_of":"2026-08-15T19:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f643b5e46b7db09487e920b00561b128333810da75e59846348e8c82a191f8f9","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:47:01.637523Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.04399/citation-record","integrity":"/paper/2506.04399/integrity","json":"/paper/2506.04399/citation-record.json","paper":"/paper/2506.04399"},"outbound":[{"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":"2026-08-07T10:47:01.527997Z","title":"Prox- imal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.527997Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:80595a0c2ed3c7010fd1d788fab9e4bb00123c957f26d8c3f1e97e992b6e532d","observation_id":"579c6bc8-2a7d-4361-ad89-3f9283335fc5","resolution":{"observed_at":"2026-08-07T10:47:01.527997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.5602","last_updated":"2013-12-19T16:00:08Z","snapshot_observed_at":"2026-08-14T03:19:40.736445Z","submitted_at":"2013-12-19T16:00:08Z","title":"Playing Atari with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5602","snapshot_observed_at":"2026-08-07T10:47:01.531930Z","title":"Playing atari with deep reinforcement learning,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.531930Z"},"links":{"cited_paper":"/paper/1312.5602","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:04d3a251196b3b72a173b628c493bf419fd60c04650881a7294a3492e6697c77","observation_id":"6d2dc8e3-3aed-4d96-8f56-1dfd00551205","resolution":{"observed_at":"2026-08-07T10:47:01.531930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.08028","last_updated":"2025-05-29T14:17:01Z","snapshot_observed_at":"2026-08-13T13:01:56.349327Z","submitted_at":"2023-01-19T12:01:41Z","title":"A Tutorial on Meta-Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.08028","snapshot_observed_at":"2026-08-07T10:47:01.535236Z","title":"A survey of meta-reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.535236Z"},"links":{"cited_paper":"/paper/2301.08028","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:4a804971456ec6f4a7b308ac0d2f3b8ca761ea2e972242fccccf9d18225e14b7","observation_id":"d3912036-18b6-4530-8977-ca777f3445fc","resolution":{"observed_at":"2026-08-07T10:47:01.535236Z","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":"2026-08-07T10:47:02.011345Z","title":"Model-agnostic meta-learning for fast adaptation of deep networks,","venue":null,"work_id":"5bca44da-4d5b-4316-946b-7d49356114d6","year":2017},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.538523Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:805faf9e6e0f8576a4986e3fee2e4fa1a4cef26f7f5de4b9df7c1e652df51462","observation_id":"08137d0e-0594-4be5-b703-4753709c0a9b","resolution":{"observed_at":"2026-08-07T10:47:02.014207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:02.002158Z","title":"A survey on offline reinforcement learning: Taxonomy, review, and open problems,","venue":null,"work_id":"0b34d8ce-b8af-43c2-b079-03756f64f006","year":2022},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.541793Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:e0602e65b6f0a9b6cc1112358ff5a958802e9b29cc690460768476290a350b3b","observation_id":"699662dc-d3b9-45ae-a91c-06dbe4e96118","resolution":{"observed_at":"2026-08-07T10:47:02.005212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01063","last_updated":"2019-03-04T04:00:38Z","snapshot_observed_at":"2026-08-14T17:08:03.096258Z","submitted_at":"2019-03-04T04:00:38Z","title":"NoRML: No-Reward Meta Learning","version":1},"cited_work":{"arxiv_id":"1903.01063","doi":null,"metadata_source":"pith","pith_arxiv_id":"1903.01063","snapshot_observed_at":"2026-08-07T10:47:01.735691Z","title":"NoRML: No-Reward Meta Learning","venue":"cs.LG","work_id":"3accbdb8-5c3c-46e4-9086-bc6b628b4c05","year":2019},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.544830Z"},"links":{"cited_paper":"/paper/1903.01063","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:702fb4f694e31950f2ceda23b2cf25d71d515d69efb60d066a49ac413cdb63dc","observation_id":"0def7e88-2112-4f73-a5e7-22cca0e1754d","resolution":{"observed_at":"2026-08-07T10:47:01.738830Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.11347","last_updated":"2019-02-27T19:23:41Z","snapshot_observed_at":"2026-08-14T19:30:50.935609Z","submitted_at":"2018-03-30T05:47:11Z","title":"Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.11347","snapshot_observed_at":"2026-08-07T10:47:01.548476Z","title":"Learning to adapt: Meta-learning for model-based control,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.548476Z"},"links":{"cited_paper":"/paper/1803.11347","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:2fee0b3d4653fd11e8fb6f9d9ff6ff5326187fdfe2cb030f051b4792456e29ef","observation_id":"ca788da2-b6d1-47a6-a4a2-db18046ea372","resolution":{"observed_at":"2026-08-07T10:47:01.548476Z","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":"2026-08-07T10:47:01.992931Z","title":"Context-aware dynamics model for generalization in model-based reinforcement learning,","venue":null,"work_id":"9995283a-b9c9-48cf-b3df-ac8f0660f1ea","year":2020},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.551512Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:acba5ba95e79ef8c19ddbe7eab43c4288ade9b1d220d03f5f0997ef69d8949da","observation_id":"5c26b27b-7f56-4fe7-9daf-3d9f61085b25","resolution":{"observed_at":"2026-08-07T10:47:01.996397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.984649Z","title":"Rma: Rapid motor adaptation for legged robots,","venue":null,"work_id":"e0b817de-e95c-43b8-a0f3-8fdd642b99f9","year":2021},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.554292Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:776a226311f3d7865269d8026dc269201e51876474c13bf203a48b0cce490975","observation_id":"704fd244-6e72-4cc5-9bbc-811e8581194d","resolution":{"observed_at":"2026-08-07T10:47:01.987339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.975673Z","title":"Efficient off- policy meta-reinforcement learning via probabilistic context variables,","venue":null,"work_id":"950c1e38-1549-4846-b3ea-220623083f96","year":2019},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.557248Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:106b8544a169567b10f58332800c0678cebbe5910176c5e37fffd71d77f8987a","observation_id":"2e7692b4-b66f-4d38-ad29-4fd0d40a6fa9","resolution":{"observed_at":"2026-08-07T10:47:01.978888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.08348","last_updated":"2020-02-27T19:40:21Z","snapshot_observed_at":"2026-08-11T22:18:09.221124Z","submitted_at":"2019-10-18T11:44:59Z","title":"VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.08348","snapshot_observed_at":"2026-08-07T10:47:01.560107Z","title":"Varibad: A very good method for bayes-adaptive deep RL via meta-learning,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.560107Z"},"links":{"cited_paper":"/paper/1910.08348","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:1f9d002a3a5ef344c206d87d0ad2b85e8bdaa771e22bb99a14d8bc37dd5cd17a","observation_id":"91a1adaa-d2c2-4a48-b4ef-bfba33e0b3a5","resolution":{"observed_at":"2026-08-07T10:47:01.560107Z","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":"2026-08-07T10:47:01.966614Z","title":"Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. Institut f ¨ur Informatik, Technische Universit¨at M ¨unchen,","venue":null,"work_id":"5d9b6f6a-c66d-46fd-a4b5-1e64c970b4e3","year":1987},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.563343Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:48624fcd7e3c758a9a6927ceb0143ed3d9c76ccaa6af47dc0842c8d9f9d997d6","observation_id":"28b96007-477d-4698-a0bc-152454c66423","resolution":{"observed_at":"2026-08-07T10:47:01.969713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.958014Z","title":"Promp: Proximal meta-policy search,","venue":null,"work_id":"1fff53cf-262a-4ea2-8c03-0334070920fa","year":2018},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.566277Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:19ce9d127ec272bba2c26b279ea4dd10ed0a5bfdfab50272cb1df4e1898bee55","observation_id":"88da9622-0ca1-4818-b65e-30d0e15987b2","resolution":{"observed_at":"2026-08-07T10:47:01.960918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.948558Z","title":"A theoretical understanding of gradient bias in meta-reinforcement learning,","venue":null,"work_id":"00346c7b-b59b-4f29-b238-481d23c3db8b","year":2022},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.568950Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:06fd77019a57169c87c39c3e94b4c0d97741d5316cd327c781dd57e687ae0c76","observation_id":"9c44c5a4-9f49-4613-892c-08a1db29b399","resolution":{"observed_at":"2026-08-07T10:47:01.951749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.938713Z","title":"A simple neural attentive meta-learner","venue":null,"work_id":"32f2a47b-d1f6-46e5-be06-6726807ffb6b","year":2018},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.571680Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:a0f593b2caf1b240a061d2bc85ebe64cb2e27c805b8aa227c65b1c73a45ef0f8","observation_id":"6ce7beec-184b-449d-9bef-83699efef3f9","resolution":{"observed_at":"2026-08-07T10:47:01.942015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.929866Z","title":"One-shot imitation learning","venue":null,"work_id":"b78ab0c6-1349-40d9-b6e7-523b7ab6da48","year":2017},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.574375Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:66a7d7e672d78a60216a4571b9dffb442858cccb902f5906fcb2c6ffdb4ad2fb","observation_id":"b6cad3d8-3323-4f17-afc6-1b086d224f85","resolution":{"observed_at":"2026-08-07T10:47:01.932749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.920091Z","title":"Meta- reinforcement learning of structured exploration strategies,","venue":null,"work_id":"b8cc8695-d805-48af-af59-6d8d42783217","year":2018},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.577198Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:433425dcc9d3ff9e2f5d5b476ac2df555bdf7636bbb7a0e7205da01a9779ff6a","observation_id":"2802b03c-ccce-4a1e-b56d-86c105be5953","resolution":{"observed_at":"2026-08-07T10:47:01.923420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.09835","last_updated":"2017-09-28T15:59:41Z","snapshot_observed_at":"2026-08-15T18:17:24.754611Z","submitted_at":"2017-07-31T13:08:11Z","title":"Meta-SGD: Learning to Learn Quickly for Few-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.09835","snapshot_observed_at":"2026-08-07T10:47:01.579854Z","title":"Meta-sgd: Learning to learn quickly for few-shot learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.579854Z"},"links":{"cited_paper":"/paper/1707.09835","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:7b5853034d49887248fee12cfa284b9b40db94bf73e05dd2307a2728c29c334f","observation_id":"7ce6fef0-727b-49a7-9001-d4648ec4c009","resolution":{"observed_at":"2026-08-07T10:47:01.579854Z","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":"2026-08-07T10:47:01.910863Z","title":"Multimodal model-agnostic meta-learning via task-aware modulation,","venue":null,"work_id":"9bf5e8d5-d9c3-4b81-b9f8-f26329cdfb4c","year":2019},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.582717Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:3eb5ce8c28d44bac761388e086322d81687c39b1d574e30914adfba669471816","observation_id":"121840d4-045c-4537-9d48-23cec949ff22","resolution":{"observed_at":"2026-08-07T10:47:01.913975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.01613","last_updated":"2018-07-04T14:36:15Z","snapshot_observed_at":"2026-08-14T18:55:45.164749Z","submitted_at":"2018-07-04T14:36:15Z","title":"Conditional Neural Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.01613","snapshot_observed_at":"2026-08-07T10:47:01.585537Z","title":"Conditional neural processes,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.585537Z"},"links":{"cited_paper":"/paper/1807.01613","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:9b090d5b28859cdd02403226cb3849ebbb1e0b063232b089d6dc7f89cad2975d","observation_id":"3628f5e7-cf2b-46a8-aad9-7d8f8ec3c8b0","resolution":{"observed_at":"2026-08-07T10:47:01.585537Z","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":"2026-08-07T10:47:01.901391Z","title":"Focal: Efficient fully-offline meta- reinforcement learning via distance metric learning and behavior reg- ularization,","venue":null,"work_id":"50b0a477-cadc-4f04-9c22-e63e09600c83","year":2020},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.588740Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:c82a2aec5845b90b529d27da22d7ec7fb0d6b4e3bd26c3df9a10b89cddd8d423","observation_id":"01ac44aa-508d-4434-947d-ae5f48197303","resolution":{"observed_at":"2026-08-07T10:47:01.904569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.591285Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.591285Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:a8d0ac4ec47e89111acb36ca402714948c311b633ebc4e77630280f73150a6e0","observation_id":"c9830779-a760-4dfc-8761-51149b6cdef3","resolution":{"observed_at":"2026-08-07T10:47:01.591285Z","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":"2026-08-07T10:47:01.885859Z","title":"Spatial multi-attention conditional neural processes,","venue":null,"work_id":"4db932e4-2fa4-4c0f-8ea5-d56fbe9a12f3","year":2024},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.594066Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:0d968291715b7365c033b259e1acf96adf6e2cebd2c131d3db8b10357bb2bd85","observation_id":"7c6221cf-026e-4270-89c5-19bba81c03f1","resolution":{"observed_at":"2026-08-07T10:47:01.889261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.876954Z","title":"Autoregressive conditional neural processes,","venue":null,"work_id":"28b2fcc1-a176-4ed3-8904-2943f9705ef4","year":2023},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.596680Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:31d1fd231d18d6ca386ed97fa923eb975e9a11b18d17b4c1e00040418ff961ae","observation_id":"fe9b722f-2a66-4445-9430-216a215f6443","resolution":{"observed_at":"2026-08-07T10:47:01.880144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.867738Z","title":"Attentive neural processes,","venue":null,"work_id":"127ec103-9170-432b-9129-b4d8ca74cd13","year":2019},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.599322Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:fca4eca8b6fd3b222962fba94555f8a305599fe0492cd389dfff496aee8b8532","observation_id":"3bba4b37-ce22-49f3-af35-14b02122e323","resolution":{"observed_at":"2026-08-07T10:47:01.870370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.859179Z","title":"Contrastive conditional neural processes,","venue":null,"work_id":"6f7bf793-bc63-4fc5-9883-f912d06f9e05","year":2022},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.601918Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:22d13ffbc27f1c0203141b824df641bcc0123bb468fe565c73abcf6c9738a492","observation_id":"188f9c31-3c96-4e79-8ea1-2540990b5eae","resolution":{"observed_at":"2026-08-07T10:47:01.862100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.850470Z","title":"Conditional neural movement primitives","venue":null,"work_id":"d97fd1fd-132b-4db0-aabe-30ca81015bff","year":2019},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.604588Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:e0797528db4f7354d9a5006334463c7fc406f72758e95efd5a9531048b373211","observation_id":"6130c372-3647-4dc8-a38b-87b6f7aa3524","resolution":{"observed_at":"2026-08-07T10:47:01.853431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.841501Z","title":"Imitation and mirror systems in robots through deep modality blending networks,","venue":null,"work_id":"aaec5eac-8cd1-493e-b85c-6e947de2bfa1","year":2022},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.607280Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:247cd1cf96d16e50079d59d06dd4d98ce81f26bf1bc2b83c348f9cb815481852","observation_id":"d1d17069-040d-45ca-a310-8ef944d674e8","resolution":{"observed_at":"2026-08-07T10:47:01.844494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.14934","last_updated":"2021-12-21T20:20:02Z","snapshot_observed_at":"2026-08-13T19:27:00.468904Z","submitted_at":"2021-11-29T20:20:29Z","title":"Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool","version":2},"cited_work":{"arxiv_id":"2111.14934","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.14934","snapshot_observed_at":"2026-08-07T10:47:01.685051Z","title":"Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool","venue":"cs.GR","work_id":"71845313-3278-415e-8238-29346c424cae","year":2021},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.610170Z"},"links":{"cited_paper":"/paper/2111.14934","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:0540c93671ce424825ce8520f7e6c2fca504d6e30589b75f16a25c3416c102c3","observation_id":"f47ddd6d-f794-466e-907b-8115a1263a74","resolution":{"observed_at":"2026-08-07T10:47:01.690039Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.832531Z","title":null,"venue":null,"work_id":"03d23019-d8c1-4c82-b299-8cd60f690a62","year":2018},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.613020Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:a887f3965f579de0670b9e089d35011ce5f1a4d1149fd120e6587006045284c2","observation_id":"5cf3238b-0754-4bc4-b69c-025046a6ddba","resolution":{"observed_at":"2026-08-07T10:47:01.835515Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1506.02438","last_updated":"2018-10-20T18:55:07Z","snapshot_observed_at":"2026-08-15T05:06:05.489107Z","submitted_at":"2015-06-08T11:12:48Z","title":"High-Dimensional Continuous Control Using Generalized Advantage Estimation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.02438","snapshot_observed_at":"2026-08-07T10:47:01.615754Z","title":"High- dimensional continuous control using generalized advantage estimation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.615754Z"},"links":{"cited_paper":"/paper/1506.02438","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:48848568253cdb99182ddb3356174323950c10f4dc14c79a241cff4980604c70","observation_id":"aebc4b54-bc2b-4ad8-a666-ef3220662cbf","resolution":{"observed_at":"2026-08-07T10:47:01.615754Z","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":"2026-08-07T10:47:01.823555Z","title":"Generalization in transfer learning: robust control of robot locomotion,","venue":null,"work_id":"31b24ebb-f1c3-422d-a5f8-1023c3bada8b","year":2022},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.618473Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:3c117de262cb684d8d5f933e8d2e0551ba59ad0cb7348f10ae5f86f18862123f","observation_id":"66cba75d-c799-43aa-815a-4be2150f1a0e","resolution":{"observed_at":"2026-08-07T10:47:01.826375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.814812Z","title":"Adam: Amethod for stochastic optimiza- tion,","venue":null,"work_id":"b8fe1968-3133-4561-a094-0bfc697a3595","year":2014},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.621180Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:2cd5d9e85f687d5194854c251aa1eea91bc8579be6d03bbfc0245d0920cd6261","observation_id":"162bad7c-49e0-447c-b2c6-3ceb39ff31d7","resolution":{"observed_at":"2026-08-07T10:47:01.817800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.805601Z","title":"Deep reinforce- ment learning in a handful of trials using probabilistic dynamics models,","venue":null,"work_id":"9724e122-b92d-4a13-bf88-da97b1f64731","year":2018},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.623680Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:c321d456dab98bd6785d2845395643706590fd806d47c4b9bb9229c2884cc03d","observation_id":"e83a7dff-dc30-4465-8c6b-5eaa2cc07937","resolution":{"observed_at":"2026-08-07T10:47:01.808744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.796394Z","title":"Mujoco: A physics engine for model- based control,","venue":null,"work_id":"97118c19-fb8f-4775-9e59-7956937807b3","year":2012},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.626258Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:7a73638b777ca79c65b5f9f10ef3533c70925fe11478fdf2cfc5faab8d8e684b","observation_id":"d570f6bd-1ca5-426a-b588-c755eee4f62e","resolution":{"observed_at":"2026-08-07T10:47:01.799343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01540","last_updated":"2016-06-05T17:54:48Z","snapshot_observed_at":"2026-08-13T12:26:05.192883Z","submitted_at":"2016-06-05T17:54:48Z","title":"OpenAI Gym","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01540","snapshot_observed_at":"2026-08-07T10:47:01.629002Z","title":"Openai gym,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.629002Z"},"links":{"cited_paper":"/paper/1606.01540","citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:d5f6161994ed78243364a30678a90fb2eac13fa885a1edc9708674d6a5d4dc69","observation_id":"bd8beb11-f5ea-43b3-8b5f-bf0bc20042ed","resolution":{"observed_at":"2026-08-07T10:47:01.629002Z","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":"2026-08-07T10:47:01.788006Z","title":"CaDM Code Repository ,","venue":null,"work_id":"a3774d96-e09a-4dd2-b143-18b053d2f39a","year":2020},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.631956Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:e809f1339c14aa3cb81540a4c75df13a7d33eb88977000f107de67bd082110ca","observation_id":"20de5e37-65ac-4952-ba35-caab4c1d4f0a","resolution":{"observed_at":"2026-08-07T10:47:01.790741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-07T10:47:01.634639Z","title":"Model predictive control: Theory and practice—a survey,","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.634639Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:12ad12b8240de22418ea2c5840c920c8a905803edfbdf384120118f385245496","observation_id":"a66394db-1e9e-4e3f-a1a6-64b40ff26d27","resolution":{"observed_at":"2026-08-07T10:47:01.634639Z","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":"2026-08-07T10:47:01.773149Z","title":"The cross- entropy method for optimization,","venue":null,"work_id":"f712cd62-ed9d-43b1-b639-f5c93f809c1f","year":2013},"citing_paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:01.637523Z"},"links":{"citing_paper":"/paper/2506.04399"},"observation_digest":"sha256:7e2af4dd15b889510184da47cac47943ea0e62ed658905d7fdbd99279e888d65","observation_id":"19ea6751-df84-412b-989f-49b47ee2e463","resolution":{"observed_at":"2026-08-07T10:47:01.776181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.04399","last_updated":"2025-06-04T19:27:47Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T17:32:18.649017Z","submitted_at":"2025-06-04T19:27:47Z","title":"Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":25},"total_outbound_references":39},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.04399."}