{"as_of":"2026-08-09T12:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0f4293d1e81c544466ce66c2249f22926f9992e7c2e0e3bc75fff707a9d46fd4","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T13:25:19.673190Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-09T11:46:12.973604Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1707.06690","last_updated":"2018-07-07T06:42:02Z","snapshot_observed_at":"2026-08-06T08:33:14.772959Z","submitted_at":"2017-07-20T19:39:23Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06690","snapshot_observed_at":"2026-08-07T13:25:19.673190Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.21926","last_updated":"2025-05-28T03:21:28Z","snapshot_observed_at":"2026-08-08T22:38:43.420698Z","submitted_at":"2025-05-28T03:21:28Z","title":"Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T13:25:19.673190Z"},"links":{"cited_paper":"/paper/1707.06690","citing_paper":"/paper/2505.21926"},"observation_digest":"sha256:5db79603b81b03ee0be0880105271a2f9b2288c527c0156b047d67b6bc5c2393","observation_id":"7c9d0f5c-e2f4-47db-8a19-217e9f5c56d5","resolution":{"observed_at":"2026-08-07T13:25:19.673190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06690","last_updated":"2018-07-07T06:42:02Z","snapshot_observed_at":"2026-08-06T08:33:14.772959Z","submitted_at":"2017-07-20T19:39:23Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06690","snapshot_observed_at":"2026-08-06T21:56:00.610818Z","title":"Deeppath: A reinforcement learning method for knowledge graph reasoning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.23137","last_updated":"2025-08-30T20:56:07Z","snapshot_observed_at":"2026-08-09T10:56:13.379832Z","submitted_at":"2025-06-29T08:22:04Z","title":"Flow-Modulated Scoring for Semantic-Aware Knowledge Graph Completion","version":3},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:00.610818Z"},"links":{"cited_paper":"/paper/1707.06690","citing_paper":"/paper/2506.23137"},"observation_digest":"sha256:979e18eab4679d35890c90ce34d909ce102918bfa0d83bf71834135f2d17827b","observation_id":"fc9b6119-bd55-4c61-bbb0-e3d14ddc357c","resolution":{"observed_at":"2026-08-06T21:56:00.610818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06690","last_updated":"2018-07-07T06:42:02Z","snapshot_observed_at":"2026-08-06T08:33:14.772959Z","submitted_at":"2017-07-20T19:39:23Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06690","snapshot_observed_at":"2026-08-06T18:45:18.103806Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.07595","last_updated":"2025-07-10T09:54:37Z","snapshot_observed_at":"2026-08-09T04:07:20.143114Z","submitted_at":"2025-07-10T09:54:37Z","title":"Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T18:45:18.103806Z"},"links":{"cited_paper":"/paper/1707.06690","citing_paper":"/paper/2507.07595"},"observation_digest":"sha256:262e365c29b605dbec4657253819f64864e11554c227528120ff648373c86866","observation_id":"6692f537-4474-4c37-b053-42691d8bd9b1","resolution":{"observed_at":"2026-08-06T18:45:18.103806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06690","last_updated":"2018-07-07T06:42:02Z","snapshot_observed_at":"2026-08-06T08:33:14.772959Z","submitted_at":"2017-07-20T19:39:23Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06690","snapshot_observed_at":"2026-08-05T10:53:13.441759Z","title":"arXiv preprint arXiv:1707.06690 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.03626","last_updated":"2025-09-03T18:29:30Z","snapshot_observed_at":"2026-08-06T09:55:44.881558Z","submitted_at":"2025-09-03T18:29:30Z","title":"Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-05T10:53:13.441759Z"},"links":{"cited_paper":"/paper/1707.06690","citing_paper":"/paper/2509.03626"},"observation_digest":"sha256:2d5f7655d2898c0b013dabad90ffa2fae4291c0143ea637436b7e547b50b8f37","observation_id":"eea8ac16-84f5-4992-98cc-0f6fb98b26b0","resolution":{"observed_at":"2026-08-05T10:53:13.441759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06690","last_updated":"2018-07-07T06:42:02Z","snapshot_observed_at":"2026-08-06T08:33:14.772959Z","submitted_at":"2017-07-20T19:39:23Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06690","snapshot_observed_at":"2026-08-03T02:33:24.810123Z","title":"arXiv preprint arXiv:1707.06690 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.21846","last_updated":"2026-07-31T09:27:09Z","snapshot_observed_at":"2026-08-08T11:46:21.528259Z","submitted_at":"2026-03-23T11:35:40Z","title":"Shaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T02:33:24.810123Z"},"links":{"cited_paper":"/paper/1707.06690","citing_paper":"/paper/2603.21846"},"observation_digest":"sha256:be658b27ab94e031f3b87c56facf7d527664a0ed8f28e3329ec31a18d39be82f","observation_id":"0c8d0c72-5ba0-4a13-8b28-6e455805656c","resolution":{"observed_at":"2026-08-03T02:33:24.810123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06690","last_updated":"2018-07-07T06:42:02Z","snapshot_observed_at":"2026-08-06T08:33:14.772959Z","submitted_at":"2017-07-20T19:39:23Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning","version":3},"cited_work":{"arxiv_id":"1707.06690","doi":null,"metadata_source":"pith","pith_arxiv_id":"1707.06690","snapshot_observed_at":"2026-07-09T11:46:12.973604Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning","venue":"cs.CL","work_id":"0ba61297-7080-4969-956b-3fa6949b4908","year":2017},"citing_paper":{"arxiv_id":"2607.07422","last_updated":"2026-07-08T13:49:44Z","snapshot_observed_at":"2026-08-08T07:22:47.541211Z","submitted_at":"2026-07-08T13:49:44Z","title":"InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-09T11:39:24.051110Z"},"links":{"cited_paper":"/paper/1707.06690","citing_paper":"/paper/2607.07422"},"observation_digest":"sha256:92db309e87636511055bd9c08e0fa4424eb61a755a4624aa493e02cdac78258b","observation_id":"66b934e7-a67c-4f0a-a5ba-4267175f38ce","resolution":{"observed_at":"2026-07-09T11:46:12.974763Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/1707.06690/citation-record","integrity":"/paper/1707.06690/integrity","json":"/paper/1707.06690/citation-record.json","paper":"/paper/1707.06690"},"outbound":[],"paper":{"arxiv_id":"1707.06690","last_updated":"2018-07-07T06:42:02Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-06T08:33:14.772959Z","submitted_at":"2017-07-20T19:39:23Z","title":"DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning"},"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":"2026-08-09T06:31:02.800959+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 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1707.06690."}