{"as_of":"2026-08-11T02:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:10130c92d1e98671a66117d69764c94765a5f6ba294fe7717e82592b4adcc6dc","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:33:39.802651Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:33:30.784788Z","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":"2026-08-06T18:33:31.494829Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"cited_work":{"arxiv_id":"2506.17466","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.17466","snapshot_observed_at":"2026-08-06T18:33:31.494829Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","venue":"cs.LG","work_id":"ce6af7bb-38c1-46c7-87ad-23e7ad0ab29e","year":2025},"citing_paper":{"arxiv_id":"2507.07901","last_updated":"2025-07-22T19:28:06Z","snapshot_observed_at":"2026-08-07T06:47:07.617752Z","submitted_at":"2025-07-10T16:33:06Z","title":"The Trust Fabric: Decentralized Interoperability and Economic Coordination for the Agentic Web","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T18:33:30.784788Z"},"links":{"cited_paper":"/paper/2506.17466","citing_paper":"/paper/2507.07901"},"observation_digest":"sha256:51b9dccd8d5413b7099b737a8c6265babbe6a6b29de5637b521d27fe73209ff6","observation_id":"959dde92-83d2-420b-bdfd-ea061eac7b84","resolution":{"observed_at":"2026-08-06T18:33:31.567023Z","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":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.17466","snapshot_observed_at":"2026-08-01T09:12:16.087452Z","title":"Fednams: Performing in- terpretability analysis in federated learning context,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.21665","last_updated":"2026-07-23T03:36:39Z","snapshot_observed_at":"2026-08-09T18:31:43.065993Z","submitted_at":"2026-07-23T03:36:39Z","title":"Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T09:12:16.087452Z"},"links":{"cited_paper":"/paper/2506.17466","citing_paper":"/paper/2607.21665"},"observation_digest":"sha256:406862477e8cf7779e1936db49d117503e31022323bb7a403fcdb456c8df1e6e","observation_id":"a00eaca0-3024-4d3a-97e6-d97accdde344","resolution":{"observed_at":"2026-08-01T09:12:16.087452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.17466/citation-record","integrity":"/paper/2506.17466/integrity","json":"/paper/2506.17466/citation-record.json","paper":"/paper/2506.17466"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:36.903221Z","title":"Neural additive models: Interpretable machine learning with neural nets","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:36.903221Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:e33e834179178db01c3174e1d1fb5af84bdabb6a06d1351483dd29cb86f22fb3","observation_id":"7c15c58d-aff3-4813-880b-062699ed06d2","resolution":{"observed_at":"2026-08-06T23:33:36.903221Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:42.294858Z","title":"Handling privacy-sensitive medical data with federated learning: challenges and future directions","venue":null,"work_id":"64331492-288f-4ad0-8e07-f1814f547437","year":2022},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:37.089962Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:88acfe9f502240af4480bc0b4e39973a04f0f16fdcba1b8ea05d8e967139e98b","observation_id":"2421b096-5dd6-4e39-841b-aa22fbc1df5e","resolution":{"observed_at":"2026-08-06T23:33:42.379125Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:42.054683Z","title":"Building communication efficient asynchronous peer-to-peer federated llms with blockchain","venue":null,"work_id":"ddbc8697-6d3a-4911-bc3b-e49ca3a7e8cf","year":2024},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:37.215390Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:292b30352c08f04fd83ddf5443e77b5da9745c605d0d8e61a894c753e9c57b5d","observation_id":"b02b2d92-bdd9-40bd-8deb-753a9daeee56","resolution":{"observed_at":"2026-08-06T23:33:42.156380Z","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":"1902.01046","last_updated":"2019-03-22T20:25:57Z","snapshot_observed_at":"2026-08-09T11:18:21.803795Z","submitted_at":"2019-02-04T06:27:41Z","title":"Towards Federated Learning at Scale: System Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01046","snapshot_observed_at":"2026-08-06T23:33:37.344580Z","title":"Towards federated learning at scale: Syste m design","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:37.344580Z"},"links":{"cited_paper":"/paper/1902.01046","citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:b591047710f70ed7c83befd9414d0b087e81dc6e84cda11a1b7175dba98ade88","observation_id":"c1467cc1-9da4-488a-a968-2877190ba9e4","resolution":{"observed_at":"2026-08-06T23:33:37.344580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15080","last_updated":"2024-02-11T11:59:52Z","snapshot_observed_at":"2026-08-10T12:41:27.810771Z","submitted_at":"2023-10-23T16:37:59Z","title":"Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15080","snapshot_observed_at":"2026-08-06T23:33:37.443353Z","title":"Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:37.443353Z"},"links":{"cited_paper":"/paper/2310.15080","citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:44aa964efc8eef240bc3111c36b03e6f9a4c652f40052343d331b7c516b4e735","observation_id":"156d8865-31d3-40d2-b415-a212815bd995","resolution":{"observed_at":"2026-08-06T23:33:37.443353Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:41.785993Z","title":"Fast federated learning in the presence of arbitrary device unavailability","venue":null,"work_id":"f12d635a-3c40-44cc-9b6f-5447f768afd2","year":2021},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:37.598873Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:2de8d02ffdb2dacab81e7f35bbc92705e82f032f1cdc802b191127f529417890","observation_id":"dcb6e6c3-e043-4eba-915d-783c872371b7","resolution":{"observed_at":"2026-08-06T23:33:41.904458Z","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":"1811.03604","last_updated":"2019-02-28T21:07:51Z","snapshot_observed_at":"2026-08-10T06:38:25.165407Z","submitted_at":"2018-11-08T18:37:03Z","title":"Federated Learning for Mobile Keyboard Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.03604","snapshot_observed_at":"2026-08-06T23:33:37.719118Z","title":"Federated learning for mobile keyboard prediction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:37.719118Z"},"links":{"cited_paper":"/paper/1811.03604","citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:6b2506cf0dd6d9d1fcc75f5036e8fe2a6e69ecb39c0236f72b1ae9698a2a1ef8","observation_id":"5761c69c-f027-4775-bdc2-6f6b864c12c2","resolution":{"observed_at":"2026-08-06T23:33:37.719118Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:41.484780Z","title":"Generalized additive models","venue":null,"work_id":"562ee9b8-9abc-42a0-a89a-56efa4b84c4a","year":2017},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:37.833887Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:6de795ade2735d82c442ab4819e52d6e69c4af3c31218c59513cbf8d844dce8b","observation_id":"c6595ee3-439f-4bf7-b6ae-9ee9283a7bde","resolution":{"observed_at":"2026-08-06T23:33:41.660312Z","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":"2308.13558","last_updated":"2023-08-24T16:05:14Z","snapshot_observed_at":"2026-08-04T03:54:45.334743Z","submitted_at":"2023-08-24T16:05:14Z","title":"Federated Learning for Computer Vision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13558","snapshot_observed_at":"2026-08-06T23:33:38.010222Z","title":"Federated learning for computer vision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.010222Z"},"links":{"cited_paper":"/paper/2308.13558","citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:23f5e0cc265572275d0fe260c745d0b4901720ecf521acecf44095122e6f970e","observation_id":"a1da5829-4ef2-474f-9641-3c0e4d1c5afc","resolution":{"observed_at":"2026-08-06T23:33:38.010222Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:41.232208Z","title":"Advances and open problems in federated learning","venue":null,"work_id":"0bf693cf-93e9-4b00-8355-7cc6f13ca640","year":2021},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.173221Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:905f35c185c32d945a1897e1cad6eabe1f6147912cdc26eb6bb22e3d17ebfc0e","observation_id":"dae6bfe6-d75a-483a-84e9-0df6c0716045","resolution":{"observed_at":"2026-08-06T23:33:41.367243Z","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":"2302.13473","last_updated":"2026-05-26T10:30:19Z","snapshot_observed_at":"2026-08-07T06:46:00.946752Z","submitted_at":"2023-02-27T02:06:18Z","title":"Towards Interpretable Federated Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13473","snapshot_observed_at":"2026-08-06T23:33:38.294193Z","title":"Towards interpretable federated learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.294193Z"},"links":{"cited_paper":"/paper/2302.13473","citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:a6fa07786e9e814e76f88cf0b9fef4a451764314edd66a8610016d33b1f743de","observation_id":"08a32291-6479-4b58-8cbf-f20384d33033","resolution":{"observed_at":"2026-08-06T23:33:38.294193Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:41.001181Z","title":"Recent advances on federated learning: A systematic survey","venue":null,"work_id":"45a58e9b-4b4c-4d52-9f56-b6c778833bc5","year":2024},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.404224Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:6f36f2026b5f88a8af731c89d90e262b08a6f6aff93ecb3abf2c341a60fdfa2a","observation_id":"9660c675-1732-42dc-a678-d6a6bc322634","resolution":{"observed_at":"2026-08-06T23:33:41.083841Z","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":"1706.06060","last_updated":"2018-02-17T01:11:50Z","snapshot_observed_at":"2026-08-04T13:29:04.846653Z","submitted_at":"2017-06-19T17:03:46Z","title":"Consistent feature attribution for tree ensembles","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06060","snapshot_observed_at":"2026-08-06T23:33:38.518000Z","title":"Consistent feature attribution for tree ensembles","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.518000Z"},"links":{"cited_paper":"/paper/1706.06060","citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:18caa593fe93cdc4addc5bce5fe421e1e4d96fd3bbb85f74a038d868ceae51f5","observation_id":"6f1a5eca-7614-4024-ae97-96bda7506331","resolution":{"observed_at":"2026-08-06T23:33:38.518000Z","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":"2026-08-06T23:33:38.669738Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.669738Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:ae331a03be5b3a24d7ff79d98b07b1587b811e6db370923f012472040743fc19","observation_id":"fa591864-c0a9-4267-a8e8-45e16f51d1fa","resolution":{"observed_at":"2026-08-06T23:33:38.669738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03599","last_updated":"2024-12-03T04:52:41Z","snapshot_observed_at":"2026-07-06T20:01:45.826971Z","submitted_at":"2024-12-03T04:52:41Z","title":"CPTQuant - A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03599","snapshot_observed_at":"2026-08-06T23:33:38.796216Z","title":"Cptquant--a novel mixed precision post-training quantization techniques for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.796216Z"},"links":{"cited_paper":"/paper/2412.03599","citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:2a712a101a7adcf52b66bb636b778226e1522ae86a80b00df005b725a9e04bcb","observation_id":"a7c5bca1-feab-4869-92d3-58ad5765094d","resolution":{"observed_at":"2026-08-06T23:33:38.796216Z","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":"2026-08-06T23:33:38.931311Z","title":"why should i trust you?","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:38.931311Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:04c1c5d2992e603dbd2ccebb8a9bac62cdefc4e165b9032ef88e8cc0e810c0e1","observation_id":"2c1de615-e528-4b75-bcaa-6b66b850e5e3","resolution":{"observed_at":"2026-08-06T23:33:38.931311Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:40.705112Z","title":"Recent methodological advances in federated learning for healthcare","venue":null,"work_id":"0f4d3c77-ea4d-4846-bcca-90ef518b5278","year":2024},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:39.031644Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:1f17b7e4922f8be090483184bf7930f76276ebebfec413f9b641eafda034cb59","observation_id":"9b99f442-87f1-4a82-af3e-a962990e22fe","resolution":{"observed_at":"2026-08-06T23:33:40.810052Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:40.386237Z","title":"A clustered federated learning method of user behavior analysis based on non-iid data","venue":null,"work_id":"f555f660-e0cb-4e7e-a021-0b4af38c8cdf","year":2023},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:39.130206Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:75e94e7cf0b936258446d1df8fd50dc7aa1d83d1a4cb931b73a4059d81139fa2","observation_id":"a2409476-bc95-43a1-8b4c-33574114c775","resolution":{"observed_at":"2026-08-06T23:33:40.516317Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:40.148474Z","title":"A survey of trustworthy federated learning: Issues, solutions, and challenges","venue":null,"work_id":"7ce7de7e-a8d7-4cdd-ba2e-63dcaa311029","year":2024},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:39.233911Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:4b39c093459f4b41f2e09ef8c40ce5fa51d0836b9e5bedf1afb783a68a9d6d47","observation_id":"50adc981-fa0d-4409-9046-e0def43c075e","resolution":{"observed_at":"2026-08-06T23:33:40.238554Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:33:39.352182Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:39.352182Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:28ba1d5aef136634520cba21b1c0e0b29b485c3e5732f5efc13688b303e8a999","observation_id":"00bc57ac-1db0-4c3e-860a-8d5b82145e26","resolution":{"observed_at":"2026-08-06T23:33:39.352182Z","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":"2026-08-06T23:33:39.522601Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:39.522601Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:9f61c27196190d681e29788d7623076e124022aaabfa004c7a29082d160daf2a","observation_id":"37f11b44-6c88-4996-a56d-70ccb19f86ed","resolution":{"observed_at":"2026-08-06T23:33:39.522601Z","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":"2026-08-06T23:33:39.685876Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:39.685876Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:2859d9282c54550b11f67eaa7d6c000119ff43d39e19535facc8a73f97f03b8c","observation_id":"eac7b3eb-410c-4a03-96ac-c532b560d69b","resolution":{"observed_at":"2026-08-06T23:33:39.685876Z","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":"2026-08-06T23:33:39.802651Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T23:33:39.802651Z"},"links":{"citing_paper":"/paper/2506.17466"},"observation_digest":"sha256:4a6afa0bf1a2849cc4471bbda3fd9570b40a28f7c298495b756a4d69dcd43c0a","observation_id":"f899e463-e054-47ad-9279-0ce3fc78f4dc","resolution":{"observed_at":"2026-08-06T23:33:39.802651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.17466","last_updated":"2025-06-20T20:14:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T06:47:40.017720Z","submitted_at":"2025-06-20T20:14:13Z","title":"FedNAMs: Performing Interpretability Analysis in Federated Learning Context"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":23},"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 23 of 23 outbound references and 2 inbound Pith citation observations for arXiv:2506.17466."}