{"as_of":"2026-08-09T21:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d57e9e49025346c74a855dedb530ce98598e6e834c8cd9b784e520667d8675c7","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T07:39:39.134822Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2606.13461/citation-record","integrity":"/paper/2606.13461/integrity","json":"/paper/2606.13461/citation-record.json","paper":"/paper/2606.13461"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T07:39:39.134822Z","title":"InProceed- ings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 67–73","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:79f5aaa1b3413d7b3bd8256d3ff1433579ce76437593b539236e0c5a1f6bf958","observation_id":"fb2d7a74-f383-4f7a-beaf-0c03cede1d2c","resolution":{"observed_at":"2026-06-27T07:39:39.134822Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T07:39:39.134822Z","title":"InFindings of the Association for Computational Linguistics: ACL 2024, pages 5907–5913","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:1312a0dad9e983ff9119f0e712a3fed6bba23f66865d5b5d1a31c5c6eeecd7d8","observation_id":"37dc8b9e-14c9-4c89-8876-6ebe3224983c","resolution":{"observed_at":"2026-06-27T07:39:39.134822Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T07:39:39.134822Z","title":"InPro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 16801– 16819","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:7c0d5b026bf24eef10b00cbc64a6ac5ae9c786c6fd072a409767635e624da177","observation_id":"12bd7ebc-cdfc-4743-8efb-8b20d2b565da","resolution":{"observed_at":"2026-06-27T07:39:39.134822Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T07:39:39.134822Z","title":"InPro- ceedings of the 2025 Conference on Empirical Meth- ods in Natural Language Processing, pages 18997– 19017","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:dfa092ea476805d4181970703d697ddba3d99683313de11cd51bda8a96fe1ca8","observation_id":"54754702-89d1-412a-b39d-e7d38fc457df","resolution":{"observed_at":"2026-06-27T07:39:39.134822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.09106","last_updated":"2016-12-01T10:08:15Z","snapshot_observed_at":"2026-08-08T11:39:44.107967Z","submitted_at":"2016-09-27T05:57:00Z","title":"HyperNetworks","version":4},"cited_work":{"arxiv_id":"1609.09106","doi":"10.48550/arxiv.1609.09106","metadata_source":"pith","pith_arxiv_id":"1609.09106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"HyperNetworks","venue":"cs.LG","work_id":"45baa084-f34a-44f4-bb45-6d2b9131cb67","year":2016},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"cited_paper":"/paper/1609.09106","citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:ce2002f376a01db545c11cb3245159aa119e3f1fa69dbc36ba76ec9829e78719","observation_id":"fa8a8303-bad1-4e21-9939-5b2bd2144892","resolution":{"observed_at":"2026-07-03T13:38:19.624272Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:0c18df1ec1ec81f5fa3b2640f30851bcdf5cafc1aaf3727b143243aa0afe7a5c","observation_id":"7396bb03-9d40-4dd9-bd66-e1f412685eb7","resolution":{"observed_at":"2026-07-03T13:38:19.619598Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01885","last_updated":"2016-06-06T19:50:47Z","snapshot_observed_at":"2026-08-09T05:53:23.612940Z","submitted_at":"2016-06-06T19:50:47Z","title":"Learning to Optimize","version":1},"cited_work":{"arxiv_id":"1606.01885","doi":null,"metadata_source":"pith","pith_arxiv_id":"1606.01885","snapshot_observed_at":"2026-07-10T02:26:43.055072Z","title":"Learning to Optimize","venue":"cs.LG","work_id":"2e174213-3cfd-45bc-ae92-7d3265901fb1","year":2016},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"cited_paper":"/paper/1606.01885","citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:4d448dbfa7d177a82407c405c9f89dc5090f8d15ac21ccc934c567599f6bc260","observation_id":"7880a698-dbc6-4439-9b02-206b0f2ab677","resolution":{"observed_at":"2026-07-03T13:38:19.613819Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"1707.06347","doi":"10.1016/j.artint.2010.12.005","metadata_source":"pith","pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proximal Policy Optimization Algorithms","venue":"cs.LG","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","year":2017},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:7eed8f6ba1269a364af4e30ae77309f338881d7ad694c31810b7ddb22cf9e0cd","observation_id":"527f9409-acdf-4f3d-8f1a-ebfcd83eefda","resolution":{"observed_at":"2026-07-03T13:38:19.616265Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.01815","last_updated":"2017-12-05T18:45:38Z","snapshot_observed_at":"2026-08-02T00:39:04.960144Z","submitted_at":"2017-12-05T18:45:38Z","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","version":1},"cited_work":{"arxiv_id":"1712.01815","doi":"10.48550/arxiv.1712.01815","metadata_source":"pith","pith_arxiv_id":"1712.01815","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","venue":"cs.AI","work_id":"9978bd70-b9dd-4eb6-928b-66c2d40da222","year":2017},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"cited_paper":"/paper/1712.01815","citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:72f744b95ec13447229f972c236596318035048b6951a15ef6c0a82b9e8c3229","observation_id":"11dff51d-ecb1-4e12-b557-eb282d694631","resolution":{"observed_at":"2026-07-03T13:38:19.621787Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-02T13:38:12.382503+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T13:38:12.382503+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.00345","last_updated":"2020-07-22T08:00:15Z","snapshot_observed_at":"2026-08-09T21:19:46.494980Z","submitted_at":"2020-04-01T11:26:27Z","title":"Editable Neural Networks","version":2},"cited_work":{"arxiv_id":"2004.00345","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2004.00345","snapshot_observed_at":"2026-07-04T02:59:25.193803Z","title":"Yixuan Su, Tian Lan, Huayang Li, Jialu Xu, Yan Wang, and Deng Cai","venue":null,"work_id":"37a930de-0c06-48ad-a745-40ca83c2d082","year":2004},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"cited_paper":"/paper/2004.00345","citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:c195b3044806e897a0277d3dacc5307796aeda834ba24cf0015e6b4fa27d7db8","observation_id":"b364bdf5-75d0-43aa-bfe6-c4f0a3008bf8","resolution":{"observed_at":"2026-07-03T13:38:19.611486Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T07:39:39.134822Z","title":"InInternational Conference on Learning Representations, volume 2025, pages 61897–61931","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:2df0b036ad6a5cb58b26b77cd584e2aefd48857a9c0d7ac9e7e8471283a51eaf","observation_id":"c3544ffd-e7e6-4d02-9eb2-6864b1b980c4","resolution":{"observed_at":"2026-06-27T07:39:39.134822Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T07:39:39.134822Z","title":"More recently, Chen et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:a9baf23e9d3b2865c177844b86cfbba0ee3abff5dd66114739d1c09358bccdf7","observation_id":"d6737022-8fab-4f6a-862c-d185b4bf5f51","resolution":{"observed_at":"2026-06-27T07:39:39.134822Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T07:39:39.134822Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-27T07:39:39.134822Z"},"links":{"citing_paper":"/paper/2606.13461"},"observation_digest":"sha256:4125c440d52eb5f8b6c97ea2de53fb9bbe297f600f4841e32b12ea8c477c060d","observation_id":"73664db8-0ef3-45c5-949e-9b9abcf9f0f1","resolution":{"observed_at":"2026-06-27T07:39:39.134822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.13461","last_updated":"2026-06-11T15:16:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T09:25:13.138304Z","submitted_at":"2026-06-11T15:16:42Z","title":"Reinforcement Learning for Neural Model Editing"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":13},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2606.13461."}