{"as_of":"2026-08-18T10:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fb99d6d83ed63193c9030f21cf5d974b24c81d941ba9ceacdc1d3517eb030832","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:43:31.324011Z","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":"2026-07-02T10:16:52.329475Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-08-12T10:15:35.332045Z","title":"Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19420","last_updated":"2025-07-06T19:04:16Z","snapshot_observed_at":"2026-08-17T15:34:26.449735Z","submitted_at":"2024-11-29T00:17:00Z","title":"RF-3DGS: Wireless Channel Modeling with Radio Radiance Field and 3D Gaussian Splatting","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T10:15:35.332045Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2411.19420"},"observation_digest":"sha256:1160a6d48b4d9ff36efbbe9b2b8b368340d6f7022ee574ea171bc2e0190d15e0","observation_id":"46ab4923-4998-4473-af32-ad39a27becbf","resolution":{"observed_at":"2026-08-12T10:15:35.332045Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-08-16T11:43:31.324011Z","title":"A deep learning framework for wireless radiation field reconstruction and channel prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14862","last_updated":"2025-04-21T05:03:19Z","snapshot_observed_at":"2026-08-17T12:22:44.132864Z","submitted_at":"2025-04-21T05:03:19Z","title":"FERMI: Flexible Radio Mapping with a Hybrid Propagation Model and Scalable Autonomous Data Collection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T11:43:31.324011Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2504.14862"},"observation_digest":"sha256:8964fa477693232d7a00b67ebb0f91349480a246570f4a2a5fb3abb1478c541c","observation_id":"9ccb0d13-afd7-4ebc-bf77-5d964ef0f232","resolution":{"observed_at":"2026-08-16T11:43:31.324011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-08-15T23:46:25.972933Z","title":"Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.06277","last_updated":"2025-05-06T19:38:33Z","snapshot_observed_at":"2026-08-17T21:25:29.305335Z","submitted_at":"2025-05-06T19:38:33Z","title":"Terahertz Spatial Wireless Channel Modeling with Radio Radiance Field","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T23:46:25.972933Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2505.06277"},"observation_digest":"sha256:3e165c16c146f532c56167104f7240a3a3e2f839c34fa9efe37c0aa334f431db","observation_id":"e8f8377b-679a-492b-a12a-afe54fb30b9c","resolution":{"observed_at":"2026-08-15T23:46:25.972933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-08-05T22:29:00.045899Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.06951","last_updated":"2025-08-09T11:57:33Z","snapshot_observed_at":"2026-08-14T09:59:58.108014Z","submitted_at":"2025-08-09T11:57:33Z","title":"SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T22:29:00.045899Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2508.06951"},"observation_digest":"sha256:ec8d5d88b795e99038c2ff699d6c54e8c21dc731a0e49e8b72caf1b5c10cc050","observation_id":"9b2deb93-6541-4217-b357-c055b5c4d071","resolution":{"observed_at":"2026-08-05T22:29:00.045899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":"2403.03241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-07-02T10:16:52.329475Z","title":"Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction","venue":null,"work_id":"a70560ce-a47a-4c40-95ae-034a0fb52be1","year":2024},"citing_paper":{"arxiv_id":"2604.07086","last_updated":"2026-04-08T13:40:29Z","snapshot_observed_at":"2026-08-02T19:39:03.855569Z","submitted_at":"2026-04-08T13:40:29Z","title":"Radio-Frequency Inverse Rendering for Wireless Environment Modeling","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-10T17:15:47.856092Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2604.07086"},"observation_digest":"sha256:bfe4d1f692e307524bcf679a47c67948316b0ef61da3ff4a9cc77e34dd18b56b","observation_id":"bb30101a-5a35-44da-89ef-1a77054dd611","resolution":{"observed_at":"2026-05-11T07:15:59.432889Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":"2403.03241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-07-02T10:16:52.329475Z","title":"Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction","venue":null,"work_id":"a70560ce-a47a-4c40-95ae-034a0fb52be1","year":2024},"citing_paper":{"arxiv_id":"2604.16558","last_updated":"2026-04-17T08:39:56Z","snapshot_observed_at":"2026-08-12T14:30:51.387656Z","submitted_at":"2026-04-17T08:39:56Z","title":"Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T08:14:04.769153Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2604.16558"},"observation_digest":"sha256:917d39839512d173b313b41d6bb1e5b2e1fcae19ef2fb50637e1a5b9754e19ea","observation_id":"1e8245af-987b-4818-827a-08f89af82853","resolution":{"observed_at":"2026-05-10T08:17:37.560265Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":"2403.03241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-07-02T10:16:52.329475Z","title":"Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction","venue":null,"work_id":"a70560ce-a47a-4c40-95ae-034a0fb52be1","year":2024},"citing_paper":{"arxiv_id":"2604.23310","last_updated":"2026-04-25T13:54:02Z","snapshot_observed_at":"2026-08-17T19:43:25.800397Z","submitted_at":"2026-04-25T13:54:02Z","title":"RadTwin: Generalizable Wireless Digital Twin for Dynamic Environments","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-08T07:14:29.391432Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2604.23310"},"observation_digest":"sha256:f0703e3f23c8185a930e944bac22d80dd3368ce882727f97c6b108ba75de08e9","observation_id":"75af5ab0-82f5-4222-b769-fb16df7a9806","resolution":{"observed_at":"2026-05-11T21:01:13.973555Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":"2403.03241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-07-02T10:16:52.329475Z","title":"Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction","venue":null,"work_id":"a70560ce-a47a-4c40-95ae-034a0fb52be1","year":2024},"citing_paper":{"arxiv_id":"2605.29538","last_updated":"2026-05-28T07:52:48Z","snapshot_observed_at":"2026-08-11T23:00:40.762371Z","submitted_at":"2026-05-28T07:52:48Z","title":"RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T08:44:02.202288Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2605.29538"},"observation_digest":"sha256:5bd2d78ccdb4ed4d7c6a14c5b72d787004bc5542c41764b24615610f5225451d","observation_id":"02e4959c-3cae-4384-b16c-e12a3ab38349","resolution":{"observed_at":"2026-06-29T08:53:16.422905Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","version":2},"cited_work":{"arxiv_id":"2403.03241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.03241","snapshot_observed_at":"2026-07-02T10:16:52.329475Z","title":"Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction","venue":null,"work_id":"a70560ce-a47a-4c40-95ae-034a0fb52be1","year":2024},"citing_paper":{"arxiv_id":"2606.04770","last_updated":"2026-06-03T11:52:09Z","snapshot_observed_at":"2026-08-15T01:02:36.351704Z","submitted_at":"2026-06-03T11:52:09Z","title":"WiSER: A Wireless Scene Encoder for Geometry-Grounded Multi-View Wireless Prediction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T05:09:07.754702Z"},"links":{"cited_paper":"/paper/2403.03241","citing_paper":"/paper/2606.04770"},"observation_digest":"sha256:48da2854eb6c9790fd81513666ff1dfc4117b6fd4a347cc397895754c3112f59","observation_id":"50ea55c0-3898-4573-8f92-4db58c560a71","resolution":{"observed_at":"2026-07-02T10:16:52.331037Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.03241/citation-record","integrity":"/paper/2403.03241/integrity","json":"/paper/2403.03241/citation-record.json","paper":"/paper/2403.03241"},"outbound":[],"paper":{"arxiv_id":"2403.03241","last_updated":"2024-06-14T18:16:04Z","latest_version":2,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-18T03:52:09.782817Z","submitted_at":"2024-03-05T18:55:11Z","title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2403.03241."}