{"as_of":"2026-08-11T04:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d22369812c3990364ebcb72694b246a5a3dff8a44821a887ac8c67b9b7028f5","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T20:35:27.429486Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2501.08002/citation-record","integrity":"/paper/2501.08002/integrity","json":"/paper/2501.08002/citation-record.json","paper":"/paper/2501.08002"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:35:28.052490Z","title":"GPT-4, AGI, and the hunt for superintelligence,","venue":null,"work_id":"7f461628-e7ef-4839-b5ac-7ea301765b34","year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.242520Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:82433351e9e338884f1a512246d5b1f7751f38c91183da3eaf1c6701dce4b022","observation_id":"b340b827-c8b6-403b-8cdf-2102eb23c963","resolution":{"observed_at":"2026-08-10T20:35:28.058189Z","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":"2026-08-10T20:35:28.037622Z","title":"Artificial intelligence act: MEPs adopt landmark law,","venue":null,"work_id":"db5b4351-e6e8-4b41-bfaa-4ebb964b8387","year":2024},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.247873Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:535989c011beabdfc4900c38f59e3a347ee0470b8ecf343238f2f83ff9cbc9dd","observation_id":"3a336884-c525-436a-bbca-0efd7d568026","resolution":{"observed_at":"2026-08-10T20:35:28.042222Z","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":"2026-08-10T20:35:28.023692Z","title":"A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion,","venue":null,"work_id":"856af0c4-a551-459a-9cda-bd06e9927cfc","year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.252573Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:b77602a0849efeb0337928d94e5dd71d0b0768799fa6823a9476c2ffc95c2ab5","observation_id":"ac84299a-4d44-49ea-b542-6404761403b1","resolution":{"observed_at":"2026-08-10T20:35:28.028464Z","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":"2026-08-10T20:35:28.009940Z","title":"Towards calibrated and scalable uncertainty representations for neural networks,","venue":null,"work_id":"e4cc1bd6-25aa-4ec0-a146-446b465c430c","year":2019},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.256894Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:128010500cd123eb863a9556d5a97692d60962473b7689f2d65c4d915edefa6c","observation_id":"579c07bb-1272-43b7-99a5-e760e8792a9e","resolution":{"observed_at":"2026-08-10T20:35:28.014359Z","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":"2026-08-10T20:35:27.996180Z","title":"Understanding measures of uncertainty for adversarial example detection,","venue":null,"work_id":"c3ca8668-f2c5-4cbc-ad1e-d1363b5a4ae3","year":2018},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.261180Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:e806fa8195af1a150e24382c14bd5e3e5a3bc62f33f5a1f5f0f242fb9fd5bae5","observation_id":"8affb74b-26ce-4950-8d06-6ff1bebdc70a","resolution":{"observed_at":"2026-08-10T20:35:28.000784Z","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":"2026-08-10T20:35:27.982177Z","title":"The need for uncertainty quantification in machine-assisted medical decision making,","venue":null,"work_id":"2d9856e0-1e58-4df1-96a8-e918a2f50665","year":2019},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.265300Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:428022268bbdb1ff0f0dee49b31cab823e672f523bf9627c59137baec9f3bee4","observation_id":"978e3a22-e6a4-4aa0-a7d8-9c618fa2fdc7","resolution":{"observed_at":"2026-08-10T20:35:27.986924Z","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":"2026-08-10T20:35:27.269937Z","title":"Federated learning in mobile edge networks: A comprehensive survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.269937Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:184b91adb0ce2372ee5e8b711bc709bac0318584826c6906e0deafce6eec185a","observation_id":"b3254858-e69e-43c3-a027-e3add9d96165","resolution":{"observed_at":"2026-08-10T20:35:27.269937Z","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":"2026-08-10T20:35:27.961013Z","title":"Towards personalized federated learning,","venue":null,"work_id":"cbeafed3-1e70-4496-9273-f4a33f84af2d","year":2022},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.273787Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:d88d082352042d2602d0a63a5f4d5b576d57c1af872d2fec8b18d4ba62dfafb1","observation_id":"aa42e9e7-2536-4cdc-b546-a748c367d3fd","resolution":{"observed_at":"2026-08-10T20:35:27.964931Z","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":"2026-08-10T20:35:27.948597Z","title":"An aggregation-free federated learning for tackling data heterogeneity,","venue":null,"work_id":"bc4f847e-fa99-4ff0-86bc-68fb6df969d7","year":2024},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.277665Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:c0e3cdaf6b4f4a0412a143d009c79ae89a00a4d4846081e36b4c44c7f9867fd2","observation_id":"e9849965-c0a1-435b-9700-2cbc08718f1f","resolution":{"observed_at":"2026-08-10T20:35:27.952419Z","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":"2026-08-10T20:35:27.936554Z","title":"FedHealth: A federated transfer learning framework for wearable healthcare,","venue":null,"work_id":"1068d83e-7e06-4df2-8fb1-2a5c1352f1e2","year":2020},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.281425Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:062aa6d0c3892c7a16392723c0bf1144b7622149e9fa3513973d6b0b820a3ba6","observation_id":"f3b64a47-6476-4e6b-a6c8-6b3e79d76703","resolution":{"observed_at":"2026-08-10T20:35:27.940483Z","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":"2026-08-10T20:35:27.923539Z","title":"Wireless distributed learning: A new hybrid split and federated learning approach,","venue":null,"work_id":"32d3e114-b447-49d1-b01f-dd07f88ba3dd","year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.285200Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:d2f48f61a8b4fdca82b48dfa48f1f1d5b89f0f3640be80da99fca80d7140a269","observation_id":"d8d7bfb0-14f1-423e-8ad0-0462158052fa","resolution":{"observed_at":"2026-08-10T20:35:27.927879Z","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":"2026-08-10T20:35:27.909781Z","title":"Communication-efficient federated learning and permissioned blockchain for digital twin edge networks,","venue":null,"work_id":"eb9deead-e2b6-4f86-bcff-ae95b1463e4e","year":2021},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.289654Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:84bbbf51bc58aaacf4b7260f9cc63ab9bad591365385a3f12294def196a007e3","observation_id":"cf29075c-253d-49f5-98dc-2c868cb083fc","resolution":{"observed_at":"2026-08-10T20:35:27.914158Z","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":"2026-08-10T20:35:27.896534Z","title":"Wild patterns: Ten years after the rise of adversarial machine learning,","venue":null,"work_id":"58e25bac-6155-458c-9b6e-b23378e7ef90","year":2018},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.293682Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:bae7ef4bbd2aedcc636045ebb6886c1d800c153678ceb67dc0067215a284ba3b","observation_id":"fbc56034-012d-475f-9be8-67b68ef42307","resolution":{"observed_at":"2026-08-10T20:35:27.900899Z","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":"2026-08-10T20:35:27.883118Z","title":"Investigation of deep learning architectures and features for adversarial machine learning attacks in modulation classifications,","venue":null,"work_id":"083953e0-4f6b-4c8c-b2a3-14c3cfa0f4f8","year":2022},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.297816Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:30c4f679e5ffde1b9427d14d0c0e7d02ddcb3721805cc49811564b6b66fdf031","observation_id":"89f352b6-f062-4e99-8a30-5715714c0077","resolution":{"observed_at":"2026-08-10T20:35:27.887652Z","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":"2026-08-10T20:35:27.869442Z","title":"Evidential classification for defending against adversarial examples in radio signal classification,","venue":null,"work_id":"cbc27efa-2778-4568-ae03-2b63a0928802","year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.303183Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:e11c5765b38e100436f04e94fae1b61180af925f01b363b302984232cd8208d6","observation_id":"07e58636-9eca-4931-b78b-6f2cb8f6346a","resolution":{"observed_at":"2026-08-10T20:35:27.873938Z","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":"2026-08-10T20:35:27.855263Z","title":"Bayesian optimisation-driven adversarial poisoning attacks against distributed learning,","venue":null,"work_id":"388e465a-684b-45e2-bcc2-f8f9ef70dabd","year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.307437Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:87e02a525c1f19181b5d0d78c1568024230b39867b8eba3535266b9d4a3d810b","observation_id":"e6dc298d-8841-46cb-925a-ea96f09da71d","resolution":{"observed_at":"2026-08-10T20:35:27.859937Z","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":"2026-08-10T20:35:27.840617Z","title":"Local model poisoning attacks to Byzantine-robust federated learning,","venue":null,"work_id":"19849938-0ebb-4258-b361-e4062b284fef","year":2020},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.311646Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:e0ebbba26b4ccbef272e5758c1eb8fb306255b6170ea3c3e14dcfeeb513c9cf6","observation_id":"8482b91c-65af-4ca9-bca8-fdd8573714c2","resolution":{"observed_at":"2026-08-10T20:35:27.845108Z","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":"2026-08-10T20:35:27.827167Z","title":"Membership inference attacks against machine learning models,","venue":null,"work_id":"e3544ee7-7122-4b06-adff-1daf206a3b9b","year":2017},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.315774Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:dfcfdce230cec7bae8a25c412a2e56480ab30772e4f04b392c024aaa5826462e","observation_id":"1b0f2636-6509-4fdb-9b6b-bb8aa8ef137a","resolution":{"observed_at":"2026-08-10T20:35:27.831470Z","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":"2026-08-10T20:35:27.814018Z","title":"TrustFed: A framework for fair and trustworthy cross-device federated learning in IIoT,","venue":null,"work_id":"30e0cf30-0ada-47dc-afcb-636904a6aeea","year":2021},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.320082Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:631cd559c27c906921e57ec9f69cf8d3bf22f87b559ee2c38c69d2d32f37bab8","observation_id":"091de492-09a4-4d77-9c2a-31a74842d413","resolution":{"observed_at":"2026-08-10T20:35:27.818355Z","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":"2026-08-10T20:35:27.801283Z","title":"Distributed intelligence in wireless networks,","venue":null,"work_id":"bbd26fbf-7524-4d3a-9ef5-e4dafacf69e1","year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.324609Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:e7e0745f472e5b3d037ca30c6d7891cd695f3a5743a1462c4ef85017e3bf53df","observation_id":"2a326caa-2189-4cdc-99ac-ff4499906c65","resolution":{"observed_at":"2026-08-10T20:35:27.805257Z","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":"2026-08-10T20:35:27.788567Z","title":"MPAF: Model poisoning attacks to federated learning based on fake clients,","venue":null,"work_id":"67fa996c-2f8d-41e7-96d9-a9a281ac7c4c","year":2022},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.328796Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:36feb7400d9acd198a947a611a6f8ac1e75f5735154a8feaaf0cbf0280289a6f","observation_id":"90e55c6d-1b70-44ee-a63f-5aa9c85c46ae","resolution":{"observed_at":"2026-08-10T20:35:27.792706Z","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":"2026-08-10T20:35:27.332894Z","title":"A little is enough: Circumvent- ing defenses for distributed learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.332894Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:c8f383038a2396097ec5696ab92ca19f9a669f8089591fe7beda54ebade13109","observation_id":"5861a2d7-c2e9-491c-bfa2-db717e0b8caf","resolution":{"observed_at":"2026-08-10T20:35:27.332894Z","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":"2026-08-10T20:35:27.766984Z","title":"Data poisoning attacks on federated learning by using adversarial samples,","venue":null,"work_id":"cf0c74a8-5985-4b7a-9119-7895171c4734","year":2022},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.337008Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:c2985340a44303d24c2c8f04e77df650bb1a04e33eb74ae276a20a4dc5b903aa","observation_id":"3c62665c-b45e-4427-b343-a3e72ac3184d","resolution":{"observed_at":"2026-08-10T20:35:27.770979Z","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":"2026-08-10T20:35:27.753156Z","title":"Data-agnostic model poisoning against federated learning: A graph autoencoder approach,","venue":null,"work_id":"eb0eea69-a425-48bb-9e31-265e623bd18b","year":2024},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.341020Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:16c24a31afab64713907ea338b65e81418a48839d82fc92370635c20889636ba","observation_id":"4ee0b1a5-b510-4090-a466-3e3895313259","resolution":{"observed_at":"2026-08-10T20:35:27.757983Z","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":"2026-08-10T20:35:27.738382Z","title":"Hidden trigger backdoor attacks,","venue":null,"work_id":"458eebdf-3953-46e7-9fb0-d72ffc867bd5","year":2020},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.345031Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:e95377353d51f6af1f05d084a0cba0601bd8e0551e40e08430ba466f226914a3","observation_id":"15e6a5ed-1503-43e5-a7a4-c5be0b4128ac","resolution":{"observed_at":"2026-08-10T20:35:27.742766Z","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":"1807.00459","last_updated":"2019-08-06T04:36:45Z","snapshot_observed_at":"2026-08-07T03:33:38.635570Z","submitted_at":"2018-07-02T04:37:43Z","title":"How To Backdoor Federated Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.00459","snapshot_observed_at":"2026-08-10T20:35:27.349264Z","title":"How to backdoor federated learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.349264Z"},"links":{"cited_paper":"/paper/1807.00459","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:cde688683c4ce43212f865acb2fccca376af79e776979ceb287e250464d19baf","observation_id":"8fb72856-5070-470e-a8d3-d0bbefa6ef2a","resolution":{"observed_at":"2026-08-10T20:35:27.349264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.04221","last_updated":"2021-06-15T20:54:14Z","snapshot_observed_at":"2026-08-11T01:40:39.867137Z","submitted_at":"2020-12-08T05:15:39Z","title":"Ditto: Fair and Robust Federated Learning Through Personalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.04221","snapshot_observed_at":"2026-08-10T20:35:27.353870Z","title":"Ditto: Fair and robust feder- ated learning through personalization,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.353870Z"},"links":{"cited_paper":"/paper/2012.04221","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:0058b5167b0e7fe9110ec1a7ba4444e594e06f3b06357aa13b738ff61a4ad736","observation_id":"264bbaf5-fea6-4e54-a442-b2f8df9e2c01","resolution":{"observed_at":"2026-08-10T20:35:27.353870Z","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":"2026-08-10T20:35:27.724720Z","title":"Manipulating the byzantine: Opti- mizing model poisoning attacks and defenses for federated learning,","venue":null,"work_id":"6b3e4890-cfc5-4510-ab90-d2b1eee2674c","year":2021},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.358410Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:f4d9604ce99152aba8155947d7de23073535adbfe9a06c034952e44364af2d27","observation_id":"9311647c-d799-4fc5-a2c6-1554349b9f3a","resolution":{"observed_at":"2026-08-10T20:35:27.729250Z","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":"2026-08-10T20:35:27.710925Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":"0aeeff50-8604-4b9d-b659-6d95dfa6e696","year":2014},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.362356Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:b56e229edcc6ef6d3ebbe6426c4b0ca5eae61866b0d252b0f1f1f3271a49ead3","observation_id":"6712176a-05cf-460a-827c-e12049589713","resolution":{"observed_at":"2026-08-10T20:35:27.715629Z","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":"2026-08-10T20:35:27.697147Z","title":"Coun- termeasures against adversarial examples in radio signal classification,","venue":null,"work_id":"63dde2df-92f4-4e89-a2b8-30f6381e3845","year":2021},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.366310Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:3172ff8f3aee2a043a7518d9eb82e1e41f31b90048db70334da17e64e4669ddb","observation_id":"3d2c786a-c3d5-4d9b-8036-2a3edbcb6093","resolution":{"observed_at":"2026-08-10T20:35:27.701556Z","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":"1710.06963","last_updated":"2018-02-24T00:40:30Z","snapshot_observed_at":"2026-08-10T06:02:31.713188Z","submitted_at":"2017-10-18T23:46:57Z","title":"Learning Differentially Private Recurrent Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.06963","snapshot_observed_at":"2026-08-10T20:35:27.370262Z","title":"Learn- ing differentially private recurrent language models,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.370262Z"},"links":{"cited_paper":"/paper/1710.06963","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:ea4b9bcc5900b12645d90e01be1088ca874b7377e725ea7ee966b88c5e6ac812","observation_id":"3836662d-fe0b-45e0-8e9a-f788b29349a8","resolution":{"observed_at":"2026-08-10T20:35:27.370262Z","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":"2026-08-10T20:35:27.683062Z","title":"Machine learning with adversaries: Byzantine tolerant gradient descent,","venue":null,"work_id":"096b61d6-0472-4657-a29e-573a52c6bd9e","year":2017},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.374632Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:24cc78c420d9cd1aeccb281196f5da0c6149e5de80d2f560cae1f79916c73711","observation_id":"2d546258-f450-4c8c-8902-f7b91fd56a92","resolution":{"observed_at":"2026-08-10T20:35:27.687761Z","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":"2026-08-10T20:35:27.378456Z","title":"A subspace, interior, and con- jugate gradient method for large-scale bound-constrained minimization problems,","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.378456Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:47a7a54f16c7a60509eb30580626a9aed4d2d588fd9b1119c3890216b97dcdcd","observation_id":"73c90ed8-0679-4905-9dab-2c4dea9c3839","resolution":{"observed_at":"2026-08-10T20:35:27.378456Z","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":"2026-08-10T20:35:27.659505Z","title":"An interior trust region approach for nonlinear minimization subject to bounds,","venue":null,"work_id":"7bb08f9d-c21a-4f27-a0ec-d0d2c1796b6f","year":1996},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.382373Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:92593abb81929ce9ac24433a594eefb70aa4f243299c01d6ca41477592701eb5","observation_id":"0187d6ac-5d44-4bce-b241-1c0252e62ea3","resolution":{"observed_at":"2026-08-10T20:35:27.664119Z","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":"2026-08-10T20:35:27.645263Z","title":"Efficient global optimization of expensive black-box functions,","venue":null,"work_id":"f8063f8a-3c05-4866-bdfe-13a1b5af33cb","year":1998},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.386385Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:2b5d5b53f38a3f0aea2d902e298f3f0941a33b47e0b700395dc2aadb26a98e34","observation_id":"2c6d9026-9ef3-43dc-8826-8a8e460739ad","resolution":{"observed_at":"2026-08-10T20:35:27.650376Z","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":"1206.2944","last_updated":"2012-08-29T06:36:23Z","snapshot_observed_at":"2026-08-03T20:03:26.912160Z","submitted_at":"2012-06-13T21:23:15Z","title":"Practical Bayesian Optimization of Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1206.2944","snapshot_observed_at":"2026-08-10T20:35:27.390810Z","title":"Practical Bayesian optimiza- tion of machine learning algorithms,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.390810Z"},"links":{"cited_paper":"/paper/1206.2944","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:410f7ea7a83250d00384130473e693af48269d56bee4fa17016d0394cf3f544c","observation_id":"c308a902-4971-4352-a3ae-b964b413957f","resolution":{"observed_at":"2026-08-10T20:35:27.390810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.02811","last_updated":"2018-07-08T13:06:26Z","snapshot_observed_at":"2026-08-07T12:49:05.688504Z","submitted_at":"2018-07-08T13:06:26Z","title":"A Tutorial on Bayesian Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.02811","snapshot_observed_at":"2026-08-10T20:35:27.395254Z","title":"A tutorial on Bayesian optimization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.395254Z"},"links":{"cited_paper":"/paper/1807.02811","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:85a8112bb1ced136ec82d7ed23f2e509d96ad6e0fb22148f5a6dc8dbc375baea","observation_id":"a701ee16-cac0-4f6f-825a-6a54382de723","resolution":{"observed_at":"2026-08-10T20:35:27.395254Z","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":"2026-08-10T20:35:27.631568Z","title":null,"venue":null,"work_id":"02534af7-0d60-4ece-b78f-7d466ae6144a","year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.399218Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:08d5b8aaa47bdf8ab49fec0b67ca1b4b88a3840372a3657d78014ae60512f79f","observation_id":"0b733c2e-01b4-489e-bdaa-cf38dc73edb6","resolution":{"observed_at":"2026-08-10T20:35:27.635898Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1502.05700","last_updated":"2015-07-13T15:47:13Z","snapshot_observed_at":"2026-08-03T18:04:02.374555Z","submitted_at":"2015-02-19T20:51:27Z","title":"Scalable Bayesian Optimization Using Deep Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.05700","snapshot_observed_at":"2026-08-10T20:35:27.403435Z","title":"Scalable Bayesian opti- mization using deep neural networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.403435Z"},"links":{"cited_paper":"/paper/1502.05700","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:695de107fe6121132e7fea8f143271cdca4e91562a7e5e1d409f10cddc6fc670","observation_id":"dd6cec06-0ff1-46f4-b5f4-5b6c3753760a","resolution":{"observed_at":"2026-08-10T20:35:27.403435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.12280","last_updated":"2019-06-21T10:59:58Z","snapshot_observed_at":"2026-07-06T07:56:19.842919Z","submitted_at":"2019-05-29T09:05:29Z","title":"Lifelong Bayesian Optimization","version":2},"cited_work":{"arxiv_id":"1905.12280","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.12280","snapshot_observed_at":"2026-08-10T20:35:27.505865Z","title":"Lifelong Bayesian Optimization","venue":"stat.ML","work_id":"c151d43e-bd04-49e2-b008-ec58a3cec1d3","year":2019},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.407771Z"},"links":{"cited_paper":"/paper/1905.12280","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:7c8c031c12fe4aa33bca13e8bd8ea1a672c0c3de114199591758c2bb51367fb9","observation_id":"a056e054-7a16-486b-9824-cb3fd2970737","resolution":{"observed_at":"2026-08-10T20:35:27.512818Z","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":"1712.00424","last_updated":"2017-12-01T17:32:01Z","snapshot_observed_at":"2026-07-06T06:12:18.811024Z","submitted_at":"2017-12-01T17:32:01Z","title":"The reparameterization trick for acquisition functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.00424","snapshot_observed_at":"2026-08-10T20:35:27.412155Z","title":"The reparameterization trick for acquisition functions,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.412155Z"},"links":{"cited_paper":"/paper/1712.00424","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:931e70ecfe7bfb9eaa882bf3b51630879147f237f861f9e8595fa4cd379cb35d","observation_id":"31594cf4-51bb-4e80-868a-cc0f8d57c183","resolution":{"observed_at":"2026-08-10T20:35:27.412155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-10T20:35:27.416498Z","title":"Auto-encoding variational Bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.416498Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:f13afc4dab2b486710cb789a115fa944f9fbd2c20c6fc94491ead18a221ac3a2","observation_id":"d3c7ec3e-0aff-4aae-b7b3-76267d1643c9","resolution":{"observed_at":"2026-08-10T20:35:27.416498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04992","last_updated":"2025-02-27T04:41:34Z","snapshot_observed_at":"2026-07-06T16:58:47.790985Z","submitted_at":"2023-12-08T12:03:08Z","title":"PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04992","snapshot_observed_at":"2026-08-10T20:35:27.420700Z","title":"Pfllib: Personalized federated learning algorithm library,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.420700Z"},"links":{"cited_paper":"/paper/2312.04992","citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:c061e8304a606e738202d3947893ef33f1e9ba03a7d555ffc757c7613ef2bee3","observation_id":"69aa6662-50fb-4f4a-8ea7-b56a3af02342","resolution":{"observed_at":"2026-08-10T20:35:27.420700Z","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":"2026-08-10T20:35:27.617639Z","title":"ImageNet classification with deep convolutional neural networks,","venue":null,"work_id":"d85ea9b6-0d7b-4050-9136-62ba6e76a902","year":2017},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.425105Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:765d6d4541eef891fd336fcfa01d7e1a158d28019757e0e582092a90b052a000","observation_id":"79ccc57b-7559-49ce-aca8-4e2f1d75a99e","resolution":{"observed_at":"2026-08-10T20:35:27.622232Z","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":"2026-08-10T20:35:27.603617Z","title":"His research interests include decentralized computing, federated learning, privacy- preserving AI and Blockchain","venue":null,"work_id":"6f573999-92f2-4a2f-9e06-2ab296ce19fe","year":null},"citing_paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning","version":2},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-10T20:35:27.429486Z"},"links":{"citing_paper":"/paper/2501.08002"},"observation_digest":"sha256:dd41da04bda19c562431cb57e2a27c292c6dc5893d0f8de444b70b92fa4c7eec","observation_id":"bdfbfb4f-4967-4182-80a0-403c1c568554","resolution":{"observed_at":"2026-08-10T20:35:27.608387Z","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"}}],"paper":{"arxiv_id":"2501.08002","last_updated":"2025-01-15T11:52:29Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T01:40:50.665729Z","submitted_at":"2025-01-14T10:46:41Z","title":"Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":31},"total_outbound_references":45},"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 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2501.08002."}