{"as_of":"2026-08-16T15:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7066f2ed7c3a55d9f65fbe6eb255ca9f779d1ac04d1756993d9c1574016d6909","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:57:32.520644Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2501.03448/citation-record","integrity":"/paper/2501.03448/integrity","json":"/paper/2501.03448/citation-record.json","paper":"/paper/2501.03448"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:57:32.463657Z","title":"Wireless communi cations for collaborative federated learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.463657Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:fc80d2afe41b25722e283e1ca6fbbde742eefbb0f66893913564b4c76ed392a5","observation_id":"93285d09-98c2-4f9a-852a-6b2077d2799f","resolution":{"observed_at":"2026-08-10T21:57:32.463657Z","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":"2026-08-10T21:57:32.705935Z","title":"Client s election and cost-efﬁcient joint optimization for NOMA-enabled hie rarchical federated learning,","venue":null,"work_id":"152ac813-e73c-4e71-b3e6-e0f01bd1b0a8","year":2024},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.468304Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:67e824730b4bffd0589c2e27f554ead92308ddcfdd067748ba15d1b59e87c4da","observation_id":"c441f8d7-054d-44fa-a7d6-6b6ab3d55b8e","resolution":{"observed_at":"2026-08-10T21:57:32.710042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.694212Z","title":"Federate d learning and meta learning: Approaches, applications, and directio ns,","venue":null,"work_id":"166d7021-2603-4ac4-8f7b-3bd7cce3000e","year":2024},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.472034Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:78e8f46c283db7096b638d18d205edfde6f9484fffffde96561847c3b3a72646","observation_id":"2d7b9f66-f52c-4893-a97c-7c523e4fbd1c","resolution":{"observed_at":"2026-08-10T21:57:32.698710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.07876","last_updated":"2019-12-14T06:39:54Z","snapshot_observed_at":"2026-08-14T19:43:29.400856Z","submitted_at":"2018-02-22T02:35:32Z","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.07876","snapshot_observed_at":"2026-08-10T21:57:32.475863Z","title":"Federated meta- learning with fast convergence and efﬁcient communication,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.475863Z"},"links":{"cited_paper":"/paper/1802.07876","citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:2631eda258042fd2b9e92d4a5f3d49d1819a6a92078ddb5d3cc9fb20a9dd73aa","observation_id":"8c963445-63e5-4d8c-8d52-a654a3b50886","resolution":{"observed_at":"2026-08-10T21:57:32.475863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12488","last_updated":"2023-01-18T08:30:06Z","snapshot_observed_at":"2026-08-12T10:37:35.790754Z","submitted_at":"2019-09-27T04:26:37Z","title":"Improving Federated Learning Personalization via Model Agnostic Meta Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12488","snapshot_observed_at":"2026-08-10T21:57:32.479906Z","title":"Improvin g feder- ated learning personalization via model agnostic meta lear ning,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.479906Z"},"links":{"cited_paper":"/paper/1909.12488","citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:a6f717099907fbc1918f19989b20c5562c3a5c0a37f6a339d5f56323ffd27113","observation_id":"3e6467b2-9c3f-4965-9bc7-bd7c85e2812d","resolution":{"observed_at":"2026-08-10T21:57:32.479906Z","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":"2026-08-10T21:57:32.682351Z","title":"Inexact-ADMM based federated meta-learning for fast and continual edge learni ng,","venue":null,"work_id":"826e7e85-34f8-4f5b-bdf4-3c4129058e4d","year":2021},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.483882Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:cbff0b8780ef49f73e873027c2bb263cb9ed3f17018371acfeedf9b85c812eda","observation_id":"752dce3d-1d68-4428-a9a8-d3cb3a4d8f81","resolution":{"observed_at":"2026-08-10T21:57:32.686423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.670121Z","title":"E fﬁcient federated meta-learning over multi-access wireless netwo rks,","venue":null,"work_id":"8f6e5d00-a8e0-4069-874b-fe4ac8210ac4","year":2022},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.487782Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:732684880bae588fb6bd36cdcdaaa41c9cd34cda5171d6f86dfd820756e263ee","observation_id":"b2d4ae9b-0850-4201-8363-383318e2b051","resolution":{"observed_at":"2026-08-10T21:57:32.674433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.659298Z","title":"Communication-efﬁcient personalized federated meta-learning in edge networks,","venue":null,"work_id":"8550a34a-9ae3-4308-b869-8f16c85c7561","year":2023},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.491514Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:a39c8a290afb403d357821eb236eef443f6ff551c70be01e24671c199a0a9278","observation_id":"3d7e7aff-165c-49b4-a18b-27d9216b0bc4","resolution":{"observed_at":"2026-08-10T21:57:32.663205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.645184Z","title":"Efﬁcient wireless tr afﬁc prediction at the edge: A federated meta-learning approach,","venue":null,"work_id":"bb354234-a40f-41c2-8097-9ff3be24788a","year":2022},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.495233Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:f0ed168fcd3cb09f87adc167651fa244a7c1620bd084743940861fbf5488cfb0","observation_id":"7867e3c2-adca-4b6b-abff-1ae449f98c42","resolution":{"observed_at":"2026-08-10T21:57:32.651184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.634637Z","title":"A blockchain-based reliable federated meta-learning for me taverse: A dual game framework,","venue":null,"work_id":"2d0d3939-32e4-4820-b666-70bc6aaa9725","year":2024},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.498739Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:a4f5899e17ddd0c0531541c3fd87d2c8b3213f0d86d899c137b51ca7f2f4d2d5","observation_id":"2011a8a4-24fa-4308-9a71-aee382ef62d1","resolution":{"observed_at":"2026-08-10T21:57:32.638481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.622842Z","title":"Personalized federated learning with theoretical guarantees: A model-agnostic me ta-learning approach,","venue":null,"work_id":"eb543830-28ab-40dc-8ea0-00251d38a9ad","year":2020},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.502231Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:03f3578bbfe02d8ae33f2e3d693b11c27b4c0d93870963084224dab40c5bdd54","observation_id":"7d796568-46f5-48f6-abd9-71f8865b6586","resolution":{"observed_at":"2026-08-10T21:57:32.626392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.612125Z","title":"Federated learning over wireless n etworks: Convergence analysis and resource allocation,","venue":null,"work_id":"ede499db-3fc0-45ad-94b9-0a5dc4e3b591","year":2021},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.505572Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:959d178ea2256c8de6cf6cb94b3b3c6d13a47120edddb1d683d79f5ff4b4b15c","observation_id":"402b42eb-5ed0-4449-8701-77b3506b2d86","resolution":{"observed_at":"2026-08-10T21:57:32.615841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-10T21:57:32.509314Z","title":"Processor design for por table systems,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.509314Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:4e3dd95adba281b50094220a74a8641640b261c577d994c0ebcbf107047e2805","observation_id":"fec4f5cc-3ed8-494a-991e-91596dedb4a4","resolution":{"observed_at":"2026-08-10T21:57:32.509314Z","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":"2026-08-10T21:57:32.595001Z","title":"A survey on non-orthogonal multiple access for 5 G networks: Research challenges and future trends,","venue":null,"work_id":"39076d93-704b-4d98-8082-056481937269","year":2017},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.513575Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:32d9fb3579da34b8c18806a46356445371dca3e1c5da7a56f24931f0f97f10c9","observation_id":"367cdf23-fa74-4154-83ef-f3cef19e9137","resolution":{"observed_at":"2026-08-10T21:57:32.598827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.03264","last_updated":"2018-10-08T03:57:39Z","snapshot_observed_at":"2026-08-14T18:18:13.481045Z","submitted_at":"2018-10-08T03:57:39Z","title":"Toward Understanding the Impact of Staleness in Distributed Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.03264","snapshot_observed_at":"2026-08-10T21:57:32.516732Z","title":"Toward understanding the impact of staleness in distributed machi ne learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.516732Z"},"links":{"cited_paper":"/paper/1810.03264","citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:07ffd49bdd6d977780d114dd74a7b68ed6b2f49a1ff87b89a1c56bacfb339169","observation_id":"70d2de43-badf-442f-b5db-bb9a53d8808e","resolution":{"observed_at":"2026-08-10T21:57:32.516732Z","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":"2026-08-10T21:57:32.581140Z","title":"A PDDQNLP algorithm for energy efﬁcient computation ofﬂoadi ng in UA V-assisted MEC,","venue":null,"work_id":"fc8a3b0e-66f4-4d96-84ba-e432d8638b22","year":2023},"citing_paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:57:32.520644Z"},"links":{"citing_paper":"/paper/2501.03448"},"observation_digest":"sha256:b83078e8ac4f0d791309199da7d879e987546dd9143010361ad9122b5a855d73","observation_id":"b199aa31-4d64-431a-b3b4-22cfd04b732b","resolution":{"observed_at":"2026-08-10T21:57:32.587473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.03448","last_updated":"2025-01-07T00:30:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T04:15:06.349685Z","submitted_at":"2025-01-07T00:30:31Z","title":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":11},"total_outbound_references":16},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2501.03448."}