{"as_of":"2026-08-14T22:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3bb28159d554e7bcc65e1db0f73a834d6a8014342efae5f60eada34ae3e6f4e5","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T10:20:26.712370Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:34:31.066415Z","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":"2026-07-04T06:09:37.534617Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.19418","snapshot_observed_at":"2026-08-06T23:34:31.066415Z","title":"Proto successor measure: Representing the space of all possible solutions of reinforcement learning, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.17518","last_updated":"2025-06-20T23:47:04Z","snapshot_observed_at":"2026-08-10T10:35:48.301748Z","submitted_at":"2025-06-20T23:47:04Z","title":"A Survey of State Representation Learning for Deep Reinforcement Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T23:34:31.066415Z"},"links":{"cited_paper":"/paper/2411.19418","citing_paper":"/paper/2506.17518"},"observation_digest":"sha256:b8cbbf5b295de2bf4090b2ddd4d4a1691668fd0b7d9f5ac7eefd6e6baf55e582","observation_id":"683c03af-f7ca-44e0-96ef-bbf2d68e1bd0","resolution":{"observed_at":"2026-08-06T23:34:31.066415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"cited_work":{"arxiv_id":"2411.19418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.19418","snapshot_observed_at":"2026-07-04T06:09:37.534617Z","title":"Proto successor measure: Representing the behavior space of an RL agent","venue":null,"work_id":"e2101019-a668-4daf-be2d-55e515f58012","year":2024},"citing_paper":{"arxiv_id":"2605.13207","last_updated":"2026-05-13T08:58:33Z","snapshot_observed_at":"2026-07-06T23:24:47.309044Z","submitted_at":"2026-05-13T08:58:33Z","title":"Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-14T20:26:35.019753Z"},"links":{"cited_paper":"/paper/2411.19418","citing_paper":"/paper/2605.13207"},"observation_digest":"sha256:5c263769fc72cbc8cd33415169cc4b9b414db4fe48ef4638b84d4662e4f0b8fe","observation_id":"de9c8fb7-a7d3-4a08-bb9e-8f5d120c6f14","resolution":{"observed_at":"2026-05-14T20:42:58.526495Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"cited_work":{"arxiv_id":"2411.19418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.19418","snapshot_observed_at":"2026-07-04T06:09:37.534617Z","title":"Proto successor measure: Representing the behavior space of an RL agent","venue":null,"work_id":"e2101019-a668-4daf-be2d-55e515f58012","year":2024},"citing_paper":{"arxiv_id":"2606.11525","last_updated":"2026-06-10T00:06:24Z","snapshot_observed_at":"2026-07-06T23:50:35.052764Z","submitted_at":"2026-06-10T00:06:24Z","title":"Learning Object Manipulation from Scratch via Contrastive Interaction","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-27T10:10:21.427118Z"},"links":{"cited_paper":"/paper/2411.19418","citing_paper":"/paper/2606.11525"},"observation_digest":"sha256:402d3e5611670a028ee472de589f1203c3ed699377ed7e4ed4100f1a7615a245","observation_id":"2e741467-c68e-47aa-83d9-7dad88a6fcb9","resolution":{"observed_at":"2026-07-03T10:17:57.429411Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"cited_work":{"arxiv_id":"2411.19418","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.19418","snapshot_observed_at":"2026-07-04T06:09:37.534617Z","title":"Proto successor measure: Representing the behavior space of an RL agent","venue":null,"work_id":"e2101019-a668-4daf-be2d-55e515f58012","year":2024},"citing_paper":{"arxiv_id":"2606.21271","last_updated":"2026-06-19T09:47:38Z","snapshot_observed_at":"2026-08-14T08:04:10.781009Z","submitted_at":"2026-06-19T09:47:38Z","title":"Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T14:48:19.683101Z"},"links":{"cited_paper":"/paper/2411.19418","citing_paper":"/paper/2606.21271"},"observation_digest":"sha256:73c44bd9164d41ca205645beb76211133e71a04c0070158031e95aea764d5414","observation_id":"7f84f37d-8904-4e54-be59-d64501a8d9c2","resolution":{"observed_at":"2026-07-04T06:09:37.537474Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.19418/citation-record","integrity":"/paper/2411.19418/integrity","json":"/paper/2411.19418/citation-record.json","paper":"/paper/2411.19418"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T10:20:26.816435Z","title":"Eigenfunctions of this graph Laplacian gives a representation for each state ϕ(s), or the state feature","venue":null,"work_id":"7131f74d-ff33-4eae-aa51-66fb57b593e7","year":2018},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.706590Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:944b2fd7a5c2f1c36faaa116cfe7db23dee43a46d15c3d2850847141529b54b1","observation_id":"801803b9-7364-469e-af8a-99a33c1a26c8","resolution":{"observed_at":"2026-08-12T10:20:26.820010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T10:20:26.805611Z","title":"Training for optimizing all reward functions in this class allows for state-features and successor-features to coemerge","venue":null,"work_id":"f76f758e-77b6-4851-9359-c7af99b47a08","year":2021},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.709586Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:bd0dfb8eecd9ef56dc6e460abd062f44bb16f64d3bf3d6b6679b88c02d02a11a","observation_id":"34b17c1a-0c69-4efb-a023-1e26e131a7b4","resolution":{"observed_at":"2026-08-12T10:20:26.809572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T10:20:26.871645Z","title":"Eysenbach, B., Salakhutdinov, R., and Levine, S","venue":null,"work_id":"b0775591-274f-470f-a7c5-9add81e860ac","year":null},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.674232Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:46905c2c27e75363b2bb5769c29685e3243126588b3ef9424561abb9f153e25a","observation_id":"145e9650-f397-4deb-892d-e9fe77898af4","resolution":{"observed_at":"2026-08-12T10:20:26.874864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.06169","last_updated":"2021-10-12T17:05:05Z","snapshot_observed_at":"2026-08-13T07:57:56.087944Z","submitted_at":"2021-10-12T17:05:05Z","title":"Offline Reinforcement Learning with Implicit Q-Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.06169","snapshot_observed_at":"2026-08-12T10:20:26.684362Z","title":"Janner, M., Fu, J., Zhang, M., and Levine, S","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.684362Z"},"links":{"cited_paper":"/paper/2110.06169","citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:76a50fe46d631d69c3ae94b81f42dec37d2f4fe55a17f4fed13aee3df59465c0","observation_id":"33d5249a-14fa-4ad0-bde0-140264e0ab35","resolution":{"observed_at":"2026-08-12T10:20:26.684362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04586","last_updated":"2018-10-10T15:25:49Z","snapshot_observed_at":"2026-08-14T18:16:42.738555Z","submitted_at":"2018-10-10T15:25:49Z","title":"The Laplacian in RL: Learning Representations with Efficient Approximations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04586","snapshot_observed_at":"2026-08-12T10:20:26.695241Z","title":"Wu, Y ., Tucker, G., and Nachum, O","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.695241Z"},"links":{"cited_paper":"/paper/1810.04586","citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:53d2ea5549bdeb2af44f55177b6abbb4326570452b4e06a0312a0e5313dcc553","observation_id":"3e17ec43-4144-41f8-99aa-aaacd7b20da6","resolution":{"observed_at":"2026-08-12T10:20:26.695241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.13425","last_updated":"2022-04-05T19:24:13Z","snapshot_observed_at":"2026-07-06T12:33:01.613365Z","submitted_at":"2022-01-31T18:39:27Z","title":"Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.13425","snapshot_observed_at":"2026-08-12T10:20:26.699646Z","title":"Wurman, P","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.699646Z"},"links":{"cited_paper":"/paper/2201.13425","citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:7c9e62e49b0ff4cc68ed2ee0ae3b9e9895b71e22e318357aeaad41998a0ee4ac","observation_id":"9993d872-9fb5-450f-b611-5fedddeaeeb2","resolution":{"observed_at":"2026-08-12T10:20:26.699646Z","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":"2026-08-12T10:20:26.828847Z","title":"20000 tra- jectories, each of length 50, are collected","venue":null,"work_id":"2a377ff4-8851-43d4-bcb0-8dc2ef1fc8ce","year":2022},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.703461Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:778576d55178e80ffdd52cd82e3d11757c9389d11ceafc11fbe92e5b8e9689d5","observation_id":"4b014714-e06f-4dbd-9b1b-52227d089c9f","resolution":{"observed_at":"2026-08-12T10:20:26.832693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T10:20:26.793633Z","title":"sd, sd -> s","venue":null,"work_id":"763d658b-483f-48ae-beae-5c22ec319042","year":null},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.712370Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:4d1c7b2636e04a911178fb5a70547c7748446669bcea7cc0cd0c8bc75be5ec90","observation_id":"26b5bfa1-8d63-4b0a-8162-4e1f6bee1eb5","resolution":{"observed_at":"2026-08-12T10:20:26.798172Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T10:20:26.882440Z","title":"Dadashi, R., Taiga, A","venue":null,"work_id":"a4a7e1d0-0d4a-4a90-bc01-b91d7a3668db","year":2019},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.670536Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:300b104a40585f924a2e28dea03cd5265c0b46a511f6cf6ab532fd54f5404006","observation_id":"0eeffdbb-f0a4-4a4b-a5bb-addf9405ac88","resolution":{"observed_at":"2026-08-12T10:20:26.887091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T10:20:26.850347Z","title":"9 Proto Successor Measure: Representing the Behavior Space of an RL Agent Hoang, C., Sohn, S., Choi, J., Carvalho, W","venue":null,"work_id":"d7b9076f-3916-4d97-bdfb-a5764a11eef9","year":null},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.681025Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:ceb59e296f44f8462667072efaaaf9e03e18a2b7fffc1e8807af2b338b147275","observation_id":"301a9097-5639-4da0-b875-2ad794fc7fc2","resolution":{"observed_at":"2026-08-12T10:20:26.853763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.07123","last_updated":"2021-01-18T15:33:26Z","snapshot_observed_at":"2026-08-09T14:57:50.184993Z","submitted_at":"2021-01-18T15:33:26Z","title":"Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.07123","snapshot_observed_at":"2026-08-12T10:20:26.665450Z","title":"Agarwal, S., Durugkar, I., Stone, P., and Zhang, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.665450Z"},"links":{"cited_paper":"/paper/2101.07123","citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:a12cfdc4d90dee75d9647c0d19d842b562f3d2dd7b89cd595d309961d943169c","observation_id":"5b3ce4fc-11f2-486c-b267-2b382ed12c5d","resolution":{"observed_at":"2026-08-12T10:20:26.665450Z","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":"2026-08-12T10:20:26.860777Z","title":"Farebrother, J., Greaves, J., Agarwal, R., Lan, C","venue":null,"work_id":"aeb017d9-76dd-4e5a-bf04-765515f7db25","year":2023},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.677578Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:b41af486bc8c32cec4f803acda94832324fae1d2179b8bc105d8e5db543a264d","observation_id":"88a0aeec-8076-47ba-afa2-2b6a59f87202","resolution":{"observed_at":"2026-08-12T10:20:26.863976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-12T10:20:26.839475Z","title":"Warde-Farley, D., de Wiele, T","venue":null,"work_id":"99fde409-129a-4d4c-822f-bfc77d1c2d6b","year":null},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.691946Z"},"links":{"citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:5314bfe8f77e500cd0106ae9a0c9dd72f9fd2e564a7fd105f11a649b2b4d86d1","observation_id":"684399d2-542a-4bb6-bdb5-e225e5130013","resolution":{"observed_at":"2026-08-12T10:20:26.842672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00690","last_updated":"2018-01-02T15:48:14Z","snapshot_observed_at":"2026-08-01T20:24:08.300098Z","submitted_at":"2018-01-02T15:48:14Z","title":"DeepMind Control Suite","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00690","snapshot_observed_at":"2026-08-12T10:20:26.688224Z","title":"Tassa, Y ., Doron, Y ., Muldal, A., Erez, T., Li, Y ., de Las Casas, D., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., Lillicrap, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent","version":2},"reference_index":9879,"source":"pdf_text","source_observed_at":"2026-08-12T10:20:26.688224Z"},"links":{"cited_paper":"/paper/1801.00690","citing_paper":"/paper/2411.19418"},"observation_digest":"sha256:57862fa6589de1c121e6a3699651c4676ad6ce2cfdc9971543f7c60c454431f2","observation_id":"3457a125-7d69-42c0-8586-49cd62777647","resolution":{"observed_at":"2026-08-12T10:20:26.688224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.19418","last_updated":"2025-03-11T17:41:54Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T12:08:53.205322Z","submitted_at":"2024-11-29T00:09:39Z","title":"Proto Successor Measure: Representing the Behavior Space of an RL Agent"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":14},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 4 inbound Pith citation observations for arXiv:2411.19418."}