{"as_of":"2026-08-11T22:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:13407c826d987031380320123da518903b1b973b5b80300e8f12f85ecbbfc993","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":"2026-08-11T12:32:01.809413Z","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":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:59:46.150573Z","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":"2026-08-10T14:59:46.196567Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"cited_work":{"arxiv_id":"2412.14119","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.14119","snapshot_observed_at":"2026-08-10T14:59:46.196567Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","venue":"cs.NI","work_id":"e3afe6d6-73f3-4248-8bbd-bf0e1ab826bc","year":2024},"citing_paper":{"arxiv_id":"2501.14619","last_updated":"2025-01-24T16:39:25Z","snapshot_observed_at":"2026-08-10T14:55:54.821401Z","submitted_at":"2025-01-24T16:39:25Z","title":"COMIX: Generalized Conflict Management in O-RAN xApps -- Architecture, Workflow, and a Power Control case","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T14:59:46.150573Z"},"links":{"cited_paper":"/paper/2412.14119","citing_paper":"/paper/2501.14619"},"observation_digest":"sha256:4a5b4e2fb91d887d0c09ff94abf2a6d6ac8c756493e7554eec9a98a7abb6b1e9","observation_id":"0ab53e98-0366-439a-8614-5e60a22fad2c","resolution":{"observed_at":"2026-08-10T14:59:46.201989Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.14119/citation-record","integrity":"/paper/2412.14119/integrity","json":"/paper/2412.14119/citation-record.json","paper":"/paper/2412.14119"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:32:02.164780Z","title":"Understanding O-RAN: Architecture, Interfaces, Al- gorithms, Security, and Research Challenges,","venue":null,"work_id":"f988971c-8a98-4f6b-8ac5-5bf00c5b301e","year":2023},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.743470Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:2d7b071be11f060f5b2caaf367639457a5f4c4ee81da4f20b48c15516c633c40","observation_id":"975f3be5-6edf-4587-903d-cdc5d838f836","resolution":{"observed_at":"2026-08-11T12:32:02.169867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.143201Z","title":"OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN,","venue":null,"work_id":"e50138b9-8db3-4a19-9255-d05831a3cc12","year":2022},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.749174Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:9e18c7c7d26752310f9af528500af50d35e08c77005a9f14ed554e948d853e88","observation_id":"ac10297f-f94f-4690-a6a5-eda86f43b184","resolution":{"observed_at":"2026-08-11T12:32:02.149989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.09619","last_updated":"2025-02-05T15:05:28Z","snapshot_observed_at":"2026-07-06T18:45:39.741597Z","submitted_at":"2024-07-12T18:05:43Z","title":"Managing O-RAN Networks: xApp Development from Zero to Hero","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.09619","snapshot_observed_at":"2026-08-11T12:32:01.754223Z","title":"Managing O-RAN Networks: xApp Development from Zero to Hero,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.754223Z"},"links":{"cited_paper":"/paper/2407.09619","citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:7974617e47b6f8a84f35db2c2f48eab095d184a2fcf72cfc7d46d8dd52021206","observation_id":"fc77686d-8d2f-413d-b9ff-ea398b8685a2","resolution":{"observed_at":"2026-08-11T12:32:01.754223Z","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":"2405.04395","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T12:32:01.929240Z","title":"PACIFISTA: Conflict Evaluation and Management in Open RAN,","venue":null,"work_id":"a5f9ccc5-35f1-4671-b9ef-0a4d11922f3e","year":2024},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.759360Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:17df3085270aed0058208c1fa86f34b298a77674bdc12bbd867588c6ff9b9055","observation_id":"0150bad6-1e35-46fb-8dbf-dd529cfe58e7","resolution":{"observed_at":"2026-08-11T12:32:01.940403Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.122134Z","title":"Conflict Management in the Near-RT-RIC of Open RAN: A Game Theoretic Approach,","venue":null,"work_id":"c2224633-5263-41c0-8e55-ff28e2629421","year":2023},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.764208Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:cf9862d459c11b896d0a0591357a50accde2f2a1079b20da9802dd7414a807e7","observation_id":"b986babc-f5a4-45bb-8c31-31dca288ab9b","resolution":{"observed_at":"2026-08-11T12:32:02.128458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.101748Z","title":"Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN),","venue":null,"work_id":"8d6b1d0e-1e0a-4706-bc4f-c6cc6f9c7cb5","year":2022},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.769202Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:a2129719021f1bdd229657d065e0882066fc987559f78fc572c3339256101964","observation_id":"46b7bd11-e99d-4e11-b5ec-845e837ac37e","resolution":{"observed_at":"2026-08-11T12:32:02.109996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.082402Z","title":"Conflict Mitigation Framework and Conflict Detection in O-RAN Near-RT RIC,","venue":null,"work_id":"708cb7f2-97b2-4271-ba34-09f76ff8271e","year":2023},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.774717Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:4607c8349df14067c9564ad23883f052130bc5c036b69874524fa26cb37f9c28","observation_id":"c6f6bbf0-8fac-4340-ad56-13e05eca6678","resolution":{"observed_at":"2026-08-11T12:32:02.088945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.060294Z","title":"Inductive Representation Learning on Large Graphs,","venue":null,"work_id":"4e73e5a7-ac68-41cd-8974-c169aa45298a","year":2017},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.784240Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:c7f26ce471feeb7d731f9e6fbcddebf4587678f14eb004ed2660d22e2ccf3fae","observation_id":"29f26d37-5621-4b6e-8913-79809b8cea08","resolution":{"observed_at":"2026-08-11T12:32:02.066719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.040714Z","title":"Toward Control and Coordination in Cognitive Autonomous Networks,","venue":null,"work_id":"9cf69ce2-cda8-4614-bf74-087893d910b7","year":2021},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.789832Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:3ba4967e515705e053a62b1a68785e086fcfe21aecb250268b43dc5d1794912e","observation_id":"820ad7a1-9429-4a84-9320-6468ffd1bd60","resolution":{"observed_at":"2026-08-11T12:32:02.047010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.021448Z","title":"TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting,","venue":null,"work_id":"c4f26fcb-2b7d-4fad-b451-7ac0dd8f7332","year":2023},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.797737Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:ab03b242d6692b12a056a5ba9ba5807082202055918c873b2f87ff3a46db5160","observation_id":"0c06c1c5-1092-4baa-a602-6fb1e2b35f52","resolution":{"observed_at":"2026-08-11T12:32:02.026716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:02.000650Z","title":"Karl Pearson and the Correlation Curve,","venue":null,"work_id":"01bf86ee-70e0-4946-9e62-4502db49559b","year":1994},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.804369Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:52e8a771191374cbe4aec14d658d292e99070796bccd401a34631fb6fd2f47bb","observation_id":"703ac479-16c7-4b9d-ad45-29ea1abaf434","resolution":{"observed_at":"2026-08-11T12:32:02.008466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T12:32:01.980202Z","title":"Near-RT RIC Architecture,","venue":null,"work_id":"363b0ab2-2fd9-4c49-9a96-03013ace4973","year":2024},"citing_paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T12:32:01.809413Z"},"links":{"citing_paper":"/paper/2412.14119"},"observation_digest":"sha256:5646134a4eb6edfe2b0e4b5303426db392ee65747f200f9119fa27ae641d05a2","observation_id":"eb39bc20-196f-4f09-8db3-53767181f9f5","resolution":{"observed_at":"2026-08-11T12:32:01.986821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.14119","last_updated":"2025-02-02T18:43:34Z","latest_version":2,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-11T12:25:42.150043Z","submitted_at":"2024-12-18T18:06:58Z","title":"Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":1,"verified_fuzzy":10},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2412.14119."}