{"as_of":"2026-08-11T01:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:21d91b5769185cac1216900ce93cda2a46d5878fe36bdf36d0b0054bb3d4a4cd","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T15:33:05.781171Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/1906.10918/citation-record","integrity":"/paper/1906.10918/integrity","json":"/paper/1906.10918/citation-record.json","paper":"/paper/1906.10918"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1606.06565","last_updated":"2016-07-25T17:23:29Z","snapshot_observed_at":"2026-07-06T05:00:46.434335Z","submitted_at":"2016-06-21T13:37:05Z","title":"Concrete Problems in AI Safety","version":2},"cited_work":{"arxiv_id":"1606.06565","doi":"10.48550/arxiv.1606.06565","metadata_source":"pith","pith_arxiv_id":"1606.06565","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Concrete Problems in AI Safety","venue":"cs.AI","work_id":"c8d14fbe-6eab-464a-95b3-778aabd82fa3","year":2016},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1606.06565","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:d6e6b9188de31b5d1abde350eac790511fcd95974e29977844fff93878f040ac","observation_id":"af487fdd-280d-4221-a344-54c868ddbde7","resolution":{"observed_at":"2026-05-25T15:35:58.990419Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-07-13T23:49:52.215761+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T23:49:52.215761+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.10720","last_updated":"2017-05-30T16:15:16Z","snapshot_observed_at":"2026-08-08T12:40:28.718338Z","submitted_at":"2017-05-30T16:15:16Z","title":"Low Impact Artificial Intelligences","version":1},"cited_work":{"arxiv_id":"1705.10720","doi":null,"metadata_source":"pith","pith_arxiv_id":"1705.10720","snapshot_observed_at":"2026-06-29T12:13:27.503706Z","title":"Low Impact Artificial Intelligences","venue":"cs.AI","work_id":"37366f11-1190-4876-9fa4-2e11dd1da731","year":2017},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1705.10720","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:27c6e1df22b602f00809070a76e88faad7ef6eb12f57d0528b79ed18b424bfcd","observation_id":"89014f71-5a23-4b64-8724-e15fc212d012","resolution":{"observed_at":"2026-05-25T15:35:58.971306Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09469","last_updated":"2020-10-06T21:20:37Z","snapshot_observed_at":"2026-08-08T11:49:19.165382Z","submitted_at":"2019-02-25T17:38:48Z","title":"Embedded Agency","version":3},"cited_work":{"arxiv_id":"1902.09469","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.09469","snapshot_observed_at":"2026-07-04T03:49:30.872226Z","title":"Embedded Agency","venue":null,"work_id":"e90d1460-e6e5-44dd-a6e6-a7410202c898","year":1902},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1902.09469","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:87df18b77e6d0d482440f14e46ba41fcb990df6d92194aa9e4576e04c31b38fc","observation_id":"a491ce4a-c08a-4c12-a0cf-89588863ae3e","resolution":{"observed_at":"2026-05-25T15:35:58.984441Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.01109","last_updated":"2018-05-21T16:30:20Z","snapshot_observed_at":"2026-08-07T04:52:04.647563Z","submitted_at":"2018-05-03T04:26:48Z","title":"AGI Safety Literature Review","version":2},"cited_work":{"arxiv_id":"1805.01109","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.01109","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"AGI Safety Literature Review","venue":"cs.AI","work_id":"9d2786b2-66a9-47e9-8dce-24a3c724199e","year":2018},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1805.01109","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:6de0f8dc1c1ea0d1e9d4f9270bf6fcf831073116ec16e5ffab0489e1c5c306bc","observation_id":"ae047e06-1dcd-427d-b85c-fb9447a5c6dc","resolution":{"observed_at":"2026-05-25T15:35:58.996514Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.01186","last_updated":"2019-03-08T09:17:21Z","snapshot_observed_at":"2026-08-10T08:36:33.840679Z","submitted_at":"2018-06-04T16:30:17Z","title":"Penalizing side effects using stepwise relative reachability","version":2},"cited_work":{"arxiv_id":"1806.01186","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.01186","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Penalizing side effects using stepwise relative reachability","venue":"cs.LG","work_id":"f636d3c0-b3d7-4de3-89c3-3ab9aff154d0","year":2018},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1806.01186","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:6719c3c17bb4f977396433ea45f19692726583fb48cb57f4f960ac99fd92c763","observation_id":"ef095c1f-ce47-425b-8423-3bfdc86426c5","resolution":{"observed_at":"2026-05-25T15:35:58.977937Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03453","last_updated":"2019-11-21T23:58:46Z","snapshot_observed_at":"2026-07-06T06:27:27.319606Z","submitted_at":"2018-03-09T10:17:18Z","title":"The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities","version":4},"cited_work":{"arxiv_id":"1803.03453","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1803.03453","snapshot_observed_at":"2026-07-02T12:46:56.197717Z","title":"The surprising creativity of digital evolution: A col- lection of anecdotes from the evolutionary computation and artiﬁcial life research communities","venue":null,"work_id":"bd2a832e-d655-4570-ac9e-cc859ea80801","year":1995},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1803.03453","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:ef8587dffbf41071da8bfe953ac4b3d8abf84401f58af52101ff79138351253f","observation_id":"451fbc69-6a32-44a5-beda-8cf00c300390","resolution":{"observed_at":"2026-05-25T15:35:59.014381Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07871","last_updated":"2018-11-19T18:48:04Z","snapshot_observed_at":"2026-08-10T22:22:50.566276Z","submitted_at":"2018-11-19T18:48:04Z","title":"Scalable agent alignment via reward modeling: a research direction","version":1},"cited_work":{"arxiv_id":"1811.07871","doi":"10.48550/arxiv.1811.07871","metadata_source":"pith","pith_arxiv_id":"1811.07871","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Scalable agent alignment via reward modeling: a research direction","venue":"cs.LG","work_id":"1e9c4f6d-b369-4bd2-8e6e-1ec00318c924","year":2018},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1811.07871","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:dcd0d64477a3d3e8a659172da7a6ca523dfdcaeafd5d6547a982be3ff356a5d6","observation_id":"d203dae5-4607-4bfa-86b9-7b75b60e10cb","resolution":{"observed_at":"2026-05-25T15:35:59.002481Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.09640","last_updated":"2018-03-23T21:53:13Z","snapshot_observed_at":"2026-07-06T06:25:31.368525Z","submitted_at":"2018-02-26T23:27:53Z","title":"Modeling Others using Oneself in Multi-Agent Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1802.09640","doi":null,"metadata_source":"pith","pith_arxiv_id":"1802.09640","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Modeling Others using Oneself in Multi-Agent Reinforcement Learning","venue":"cs.AI","work_id":"26da0743-5be5-481f-8ae0-d63e2f4e308c","year":2018},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1802.09640","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:f2cbe41e17880924b887dec6c025284331fcd5137cbdd64bbf317b3799b8d201","observation_id":"6955ca31-36b3-4e62-9011-1cd5592b6f32","resolution":{"observed_at":"2026-05-25T15:35:59.007865Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-06-05T21:23:00.469572Z","title":"Trial without error: Towards safe reinforce- ment learning via human intervention","venue":null,"work_id":"992b88e6-fa7f-4307-894b-ce5c3fe923a6","year":2067},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:2d3f4b0b75d57e45415aaf0ebd761877a9bfac1572f1fd0020ac391403d726b8","observation_id":"c8a5567a-7b15-406e-aac4-369bb58a80a2","resolution":{"observed_at":"2026-05-25T15:36:00.601017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.01703","last_updated":"2019-09-22T18:31:15Z","snapshot_observed_at":"2026-07-06T05:32:27.193419Z","submitted_at":"2017-03-06T02:02:34Z","title":"Third-Person Imitation Learning","version":2},"cited_work":{"arxiv_id":"1703.01703","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1703.01703","snapshot_observed_at":"2026-07-02T02:26:26.934636Z","title":"Third-person imitation learning","venue":null,"work_id":"b7bc6686-53bc-4247-b69c-49c9b4d2f2e3","year":2017},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1703.01703","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:d21807149faa8b448a7ffb482ebf264248d39ca9f14e86bca3d20c54dc532afe","observation_id":"125810f4-2b7d-4e91-aaad-e0d4a7655852","resolution":{"observed_at":"2026-05-25T15:35:59.020834Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09725","last_updated":"2020-06-10T15:10:04Z","snapshot_observed_at":"2026-08-06T02:41:20.031448Z","submitted_at":"2019-02-26T04:42:54Z","title":"Conservative Agency via Attainable Utility Preservation","version":3},"cited_work":{"arxiv_id":"1902.09725","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.09725","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Conservative agency via attainable utility preservation","venue":null,"work_id":"1636ace7-5956-4919-9e50-43630c4d5c50","year":1902},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"cited_paper":"/paper/1902.09725","citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:ee2ac59593fbf7fd8378454ebf4a4a6dd13d630e096b5e087055791e933059c4","observation_id":"36fe78de-0a7f-456d-8d6e-14e1b7dce635","resolution":{"observed_at":"2026-05-25T15:35:58.964613Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-06-05T21:23:00.469572Z","title":"Towards an ethical robot: internal models, consequences and ethi- cal action selection","venue":null,"work_id":"e76607bd-ac07-428c-b63d-8a9fecf0be92","year":2014},"citing_paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:05.781171Z"},"links":{"citing_paper":"/paper/1906.10918"},"observation_digest":"sha256:742c36c5d5c3f29bc24465de68bd72e49f7aa7043e6b4d29e2bb183250da0527","observation_id":"99fcc845-3660-4ce1-b2da-7a1507985e35","resolution":{"observed_at":"2026-05-25T15:36:00.596148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1906.10918","last_updated":"2019-06-26T08:59:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:02:54.458844Z","submitted_at":"2019-06-26T08:59:02Z","title":"Towards Empathic Deep Q-Learning"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":10,"verified_fuzzy":2},"total_outbound_references":12},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:1906.10918."}