{"as_of":"2026-08-19T13:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f33560436246f4df4686b6b74346bcc36f14561043ba4a8a16c2ce1c4348b112","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:17:01.996960Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-17T23:52:13.319364Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2102.06177","last_updated":"2021-06-10T18:10:47Z","snapshot_observed_at":"2026-08-16T18:46:09.162035Z","submitted_at":"2021-02-11T18:41:27Z","title":"Multi-Task Reinforcement Learning with Context-based Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.06177","snapshot_observed_at":"2026-08-11T21:17:01.996960Z","title":"Multi-task reinforcement learning with context-based representations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04847","last_updated":"2024-12-06T08:35:33Z","snapshot_observed_at":"2026-08-12T12:49:25.004835Z","submitted_at":"2024-12-06T08:35:33Z","title":"MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:01.996960Z"},"links":{"cited_paper":"/paper/2102.06177","citing_paper":"/paper/2412.04847"},"observation_digest":"sha256:6936fa6810a070a6c9b966f4d2adecbb44c76900ce7237c5b9b7c7a3435f165d","observation_id":"08d4577b-842d-4340-9db9-09a76fa86770","resolution":{"observed_at":"2026-08-11T21:17:01.996960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06177","last_updated":"2021-06-10T18:10:47Z","snapshot_observed_at":"2026-08-16T18:46:09.162035Z","submitted_at":"2021-02-11T18:41:27Z","title":"Multi-Task Reinforcement Learning with Context-based Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.06177","snapshot_observed_at":"2026-08-06T23:34:42.379983Z","title":"Multi-task reinforcement learning with context-based representations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.17518","last_updated":"2025-06-20T23:47:04Z","snapshot_observed_at":"2026-08-18T05:02:38.413531Z","submitted_at":"2025-06-20T23:47:04Z","title":"A Survey of State Representation Learning for Deep Reinforcement Learning","version":1},"reference_index":116,"source":"arxiv_source","source_observed_at":"2026-08-06T23:34:42.379983Z"},"links":{"cited_paper":"/paper/2102.06177","citing_paper":"/paper/2506.17518"},"observation_digest":"sha256:36eacccd0e6d2cf6229dcb5b9a3d19860bc40b1e52ce9e040e4ad7122f5c11f4","observation_id":"b6121c5e-f79c-4936-a199-f7e8dc059332","resolution":{"observed_at":"2026-08-06T23:34:42.379983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06177","last_updated":"2021-06-10T18:10:47Z","snapshot_observed_at":"2026-08-16T18:46:09.162035Z","submitted_at":"2021-02-11T18:41:27Z","title":"Multi-Task Reinforcement Learning with Context-based Representations","version":2},"cited_work":{"arxiv_id":"2102.06177","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.06177","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lingfeng Sun, Haichao Zhang, Wei Xu, and Masayoshi Tomizuka","venue":null,"work_id":"1c57df04-92f0-47ad-94b5-8177ab1f654e","year":2021},"citing_paper":{"arxiv_id":"2511.06371","last_updated":"2026-05-06T16:56:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-11-09T13:15:20Z","title":"Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-17T23:50:37.782513Z"},"links":{"cited_paper":"/paper/2102.06177","citing_paper":"/paper/2511.06371"},"observation_digest":"sha256:5ffe42d43eb42a892f9c41d8510d4707aa08ed483b73f6dce8183e94ae9c1b8c","observation_id":"2028d5c6-3414-4068-8260-d7d870f40197","resolution":{"observed_at":"2026-05-17T23:52:13.321743Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06177","last_updated":"2021-06-10T18:10:47Z","snapshot_observed_at":"2026-08-16T18:46:09.162035Z","submitted_at":"2021-02-11T18:41:27Z","title":"Multi-Task Reinforcement Learning with Context-based Representations","version":2},"cited_work":{"arxiv_id":"2102.06177","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.06177","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lingfeng Sun, Haichao Zhang, Wei Xu, and Masayoshi Tomizuka","venue":null,"work_id":"1c57df04-92f0-47ad-94b5-8177ab1f654e","year":2021},"citing_paper":{"arxiv_id":"2604.12645","last_updated":"2026-06-03T11:53:00Z","snapshot_observed_at":"2026-08-15T06:22:50.270387Z","submitted_at":"2026-04-14T12:16:56Z","title":"Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T15:13:16.406160Z"},"links":{"cited_paper":"/paper/2102.06177","citing_paper":"/paper/2604.12645"},"observation_digest":"sha256:1216539e521d2ee4e784f23b99d1e5b1dcb36e26e6e5be3b9eb5671fe27950dc","observation_id":"a0fd91d9-b1db-492b-a8aa-fbaaa79d17ec","resolution":{"observed_at":"2026-05-11T11:01:04.394829Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06177","last_updated":"2021-06-10T18:10:47Z","snapshot_observed_at":"2026-08-16T18:46:09.162035Z","submitted_at":"2021-02-11T18:41:27Z","title":"Multi-Task Reinforcement Learning with Context-based Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.06177","snapshot_observed_at":"2026-07-12T21:15:13.179473Z","title":"Multi-Task Reinforcement Learning with Context- based Representations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.12645","last_updated":"2026-06-03T11:53:00Z","snapshot_observed_at":"2026-08-15T06:22:50.270387Z","submitted_at":"2026-04-14T12:16:56Z","title":"Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-12T21:15:13.179473Z"},"links":{"cited_paper":"/paper/2102.06177","citing_paper":"/paper/2604.12645"},"observation_digest":"sha256:494e841663f20d1fdf11ebcdef7ae2c8e7457e9ce3bfb932a314909e50ce55cc","observation_id":"e3435608-980d-43c6-b819-2d9d079ba565","resolution":{"observed_at":"2026-07-12T21:15:13.179473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.06177","last_updated":"2021-06-10T18:10:47Z","snapshot_observed_at":"2026-08-16T18:46:09.162035Z","submitted_at":"2021-02-11T18:41:27Z","title":"Multi-Task Reinforcement Learning with Context-based Representations","version":2},"cited_work":{"arxiv_id":"2102.06177","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.06177","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lingfeng Sun, Haichao Zhang, Wei Xu, and Masayoshi Tomizuka","venue":null,"work_id":"1c57df04-92f0-47ad-94b5-8177ab1f654e","year":2021},"citing_paper":{"arxiv_id":"2605.11473","last_updated":"2026-05-12T03:40:28Z","snapshot_observed_at":"2026-08-08T22:43:14.252060Z","submitted_at":"2026-05-12T03:40:28Z","title":"TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-13T01:48:13.679862Z"},"links":{"cited_paper":"/paper/2102.06177","citing_paper":"/paper/2605.11473"},"observation_digest":"sha256:e69b6b1da26d1b4e8af00075b1dd4004ce78c9229e52ff4a6aa917de9c4b94cd","observation_id":"8a693287-e97a-4dad-882e-bf9c7af5c7fc","resolution":{"observed_at":"2026-05-13T01:52:05.694445Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2102.06177/citation-record","integrity":"/paper/2102.06177/integrity","json":"/paper/2102.06177/citation-record.json","paper":"/paper/2102.06177"},"outbound":[],"paper":{"arxiv_id":"2102.06177","last_updated":"2021-06-10T18:10:47Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T18:46:09.162035Z","submitted_at":"2021-02-11T18:41:27Z","title":"Multi-Task Reinforcement Learning with Context-based Representations"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2102.06177."}