{"as_of":"2026-08-10T15:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0484363cee6cf05c79391afbf61e8a4bd78dd40f27d4502a54dfd438d8c10096","coverage":[{"denominator":62,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":62,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:11:04.016511Z","state":"measured"},{"denominator":62,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":62,"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/2506.03302/citation-record","integrity":"/paper/2506.03302/integrity","json":"/paper/2506.03302/citation-record.json","paper":"/paper/2506.03302"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:11:04.630466Z","title":"Machine learning and the physical sciences,","venue":null,"work_id":"d18c72e9-cd89-4127-851b-d410eca685fd","year":2019},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.797188Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:0d4520e98cee21a66ec0e3ad11d9d9f6212154b76fe3f37b0cf28706b5328eff","observation_id":"8e55ec2e-027c-4482-b7cd-65b2eea4dcbf","resolution":{"observed_at":"2026-08-07T11:11:04.633215Z","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":"2026-08-07T11:11:04.622110Z","title":"Explainable machine learning for scientific insights and discoveries,","venue":null,"work_id":"337e4dd4-679d-45f1-bae4-dda03ab87052","year":2020},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.801894Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:4eca70c4f023fe4e731936cb5ef126393c65cf57143a57ce5dd23bcd49e31bbe","observation_id":"3e94b80f-30b9-4e93-bf2f-2e4db90a04d2","resolution":{"observed_at":"2026-08-07T11:11:04.625433Z","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":"2026-08-07T11:11:04.613869Z","title":"Artificial intelligence: A powerful paradigm for scientific research,","venue":null,"work_id":"cbe29d93-b8eb-447f-928f-54f5a4527679","year":null},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.805885Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:f3d0da420cbaf2d3d4559532d484627c9e689eb524e641f55bec20dd6650a7e1","observation_id":"31283614-6d88-4cf5-a8c2-4730ee447e5b","resolution":{"observed_at":"2026-08-07T11:11:04.617167Z","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":"2026-08-07T11:11:04.606486Z","title":"Scientific discovery in the age of artificial intelligence,","venue":null,"work_id":"706e903b-7e2b-4d8d-bd73-ad11a5e92790","year":2023},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.810381Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:ca7385ed24e00f1595ce98b05ac250a09f21b4f4daa5080ebd0db0661157a7a2","observation_id":"8baea6a4-683d-4105-8816-8decb8c2a956","resolution":{"observed_at":"2026-08-07T11:11:04.609094Z","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":"2026-08-07T11:11:04.592594Z","title":"Physics-informed machine learning,","venue":null,"work_id":"c6c9d163-d647-498b-86f7-1c91283885a9","year":2021},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.814215Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:1e4f8742e80dbdb1c3fbf69036012c9de987268d7be2de5e4b853d0ed308b0e8","observation_id":"c15756ab-0f8d-4014-a7cb-fe81678f7f51","resolution":{"observed_at":"2026-08-07T11:11:04.598109Z","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":"2026-08-07T11:11:04.580010Z","title":"Physics-informed machine learning: case studies for weather and climate modelling,","venue":null,"work_id":"97c8a8d8-4747-478d-8ee6-a4d85a852199","year":null},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.817976Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:47fb890ceb04cd2e2d98228977692a23141335cd5c21c60c576c524988c43a68","observation_id":"ee9221ce-cf79-4146-8cb0-c639ef77f53c","resolution":{"observed_at":"2026-08-07T11:11:04.584473Z","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":"2026-08-07T11:11:04.571916Z","title":"Learning skillful medium-range global weather forecasting,","venue":null,"work_id":"33d3bf88-f97e-4ef4-9da5-b7920101df50","year":2023},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.823107Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:e61c8ad41a1cbaeb5bfdee88dc10aa9a2842b448265e10ee75ca7660fe7f1463","observation_id":"cd527ed0-addb-4e82-91b3-5021815bd754","resolution":{"observed_at":"2026-08-07T11:11:04.574648Z","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":"2026-08-07T11:11:04.563961Z","title":"Highly accurate protein structure prediction with al- phafold,","venue":null,"work_id":"45d326fd-451c-49a5-b54f-0c967cfe83aa","year":2021},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.827612Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:e6359d443d3669816fe46b420a301b08df25d9d3ed1be3f3811abc2377a8c316","observation_id":"2beef697-6a0b-4b71-ba20-5a581c97966b","resolution":{"observed_at":"2026-08-07T11:11:04.566644Z","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":"2026-08-07T11:11:04.556074Z","title":"Data-driven modeling and learning in science and engineering,","venue":null,"work_id":"7f36d4f8-0f78-47ba-8683-3bebbb2854c4","year":null},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.832023Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:c040f614fb849f8d9b122dace51ca9b532dd346276e5de973f5e229e18810058","observation_id":"728f4bed-f85b-49d9-9045-386ea88a0912","resolution":{"observed_at":"2026-08-07T11:11:04.558999Z","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":"2026-08-07T11:11:04.539996Z","title":"Perspectives on the integration between first-principles and data-driven modeling,","venue":null,"work_id":"708dd171-f8e0-46d6-a636-5c1aeb5e299f","year":2022},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.840884Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:76a152b6afd1e1c43502159c72e0bba1d2de199a8f501cbf1c1bf8be8f604aef","observation_id":"8e71f2b3-2841-4c90-af1a-8c40fdc98263","resolution":{"observed_at":"2026-08-07T11:11:04.542873Z","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":"2026-08-07T11:11:04.531972Z","title":"It’s just not that simple: an empirical study of the accuracy-explainability trade-off in machine learning for public policy,","venue":null,"work_id":"c1d18abe-4507-4059-b51a-f56267097c2c","year":2022},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.845398Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:8de50a19d64043970d77b6ad8d3bb7c0e5b634d8ccacd214827431c6e3ec8e18","observation_id":"cd1499b2-24d6-4e98-b2a3-97ef8e0ebd25","resolution":{"observed_at":"2026-08-07T11:11:04.534821Z","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":"2026-08-07T11:11:04.524581Z","title":"How explainability contributes to trust in ai,","venue":null,"work_id":"97db589e-b0e8-4b33-b0c0-d4da5a05f9e5","year":2022},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.851885Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:73969ee1fecd12f5767f797f6b8f9678b568430267acfdb146052203b1c1a83d","observation_id":"d931b1c3-17df-4d5f-919e-ff4b68b50f96","resolution":{"observed_at":"2026-08-07T11:11:04.527033Z","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":"2026-08-07T11:11:04.517073Z","title":"Ai and science: what 1,600 researchers think,","venue":null,"work_id":"0bc9907c-2593-4617-a4fe-3d266c4187aa","year":null},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.857393Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:9817c162125030b8c121ac0289a7871476c9fbbd54617f3f9dd976432a98a749","observation_id":"a22b6e81-756e-406e-a076-91e09312a297","resolution":{"observed_at":"2026-08-07T11:11:04.519715Z","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":"2026-08-07T11:11:04.509792Z","title":"Can we open the black box of AI?,","venue":null,"work_id":"4214f7db-dd09-4e16-a0bc-ed9e8eaf2f83","year":2016},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.861493Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:a393d3a3e4fd45012a962bdb47e02a8b88d1ec8302688067bc3d60ad58ac1abe","observation_id":"36989fc1-e309-4c3b-a3ae-c40015f0edbc","resolution":{"observed_at":"2026-08-07T11:11:04.512452Z","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":"2026-08-07T11:11:04.502130Z","title":"KAN: Kolmogorov–Arnold net- works,","venue":null,"work_id":"5ad9d836-3a95-4cbb-9c7a-7f5975c7d75f","year":2025},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.865601Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:dbc238d405b8550969005dde538cd03c098dfec74bd6fcde909c98ee73a5fa61","observation_id":"926da584-8810-4236-abe3-279fbd666b2f","resolution":{"observed_at":"2026-08-07T11:11:04.504548Z","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":"2408.10205","last_updated":"2024-08-19T17:59:04Z","snapshot_observed_at":"2026-08-09T18:26:15.186638Z","submitted_at":"2024-08-19T17:59:04Z","title":"KAN 2.0: Kolmogorov-Arnold Networks Meet Science","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.10205","snapshot_observed_at":"2026-08-07T11:11:03.870677Z","title":"KAN 2.0: Kolmogorov–Arnold Networks meet science,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.870677Z"},"links":{"cited_paper":"/paper/2408.10205","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:ef279c73bb92a8c616c30b728fe9394a793e6c2004108394086cb097590c0fcc","observation_id":"b96a922c-c25a-49e1-8072-6e48999ef913","resolution":{"observed_at":"2026-08-07T11:11:03.870677Z","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":"2026-08-07T11:11:04.494815Z","title":"From PINNs to PIKANs: Recent advances in physics-informed machine learning,","venue":null,"work_id":"5de54be4-603f-436a-b5d1-f3a4cbcff370","year":2025},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.877948Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:0d1e00553e24e781cbc285367c6de4c043f1a5ecef3d4493c3f318bbf3e25129","observation_id":"6b61f538-80bf-407d-92f1-4a5cbba4a339","resolution":{"observed_at":"2026-08-07T11:11:04.497439Z","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":"2026-08-07T11:11:04.487037Z","title":"KAN-ODEs: Kolmogorov– Arnold network ordinary differential equations for learning dynamical systems and hidden physics,","venue":null,"work_id":"670f7b7b-4eb9-46bd-91d2-8ba28ba89e1c","year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.881089Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:af5173a250ae62f0904e4f6260c428be12da7bcc39e09fcb5a38c5d59705e22d","observation_id":"3d16ff32-a065-404e-944f-d5392b2bc642","resolution":{"observed_at":"2026-08-07T11:11:04.489891Z","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":"2026-08-07T11:11:04.479264Z","title":"Data-driven model discovery with Kolmogorov–Arnold networks,","venue":null,"work_id":"0413ddd6-25c8-49fc-bc4a-928b742385d0","year":2025},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.884165Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:5ce5b8b2fba4a00d936722291377edf29fc2ea097597cfe23c6cf15f553669b2","observation_id":"c0172a78-17b8-48fc-a694-7883f87319cc","resolution":{"observed_at":"2026-08-07T11:11:04.482284Z","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":"2026-08-07T11:11:04.471519Z","title":"Neural architecture search: A survey,","venue":null,"work_id":"e9df6f44-422a-438c-b454-0d1647e46804","year":2019},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.887374Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:64981138a4478947fd197b6b157afeefd14fd3f27af35b89c63011a34ca45844","observation_id":"fa2f52da-a65f-46ce-b43d-de2dada96bda","resolution":{"observed_at":"2026-08-07T11:11:04.474381Z","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":"2026-08-07T11:11:04.463128Z","title":"Deeply- supervised nets,","venue":null,"work_id":"238208aa-c4d8-4984-a36d-a4cfc7863535","year":2015},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.890089Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:696ca5334242dd51c9a96734b98c6e40c9a37fc134bca925d0de6da67e2dba4c","observation_id":"dee73901-eb23-4b6b-ab20-629d57620465","resolution":{"observed_at":"2026-08-07T11:11:04.465672Z","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":"2026-08-07T11:11:04.455532Z","title":"BranchyNet: Fast inference via early exiting from deep neural networks,","venue":null,"work_id":"65ab7d59-1e8a-431c-8184-68110206a7f7","year":2016},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.893399Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:75113fd840a8157cf342a1f3a8a2d308e4655d67829c59d474726498443c333e","observation_id":"69b417f8-2011-4203-86f8-6ebd471cb2cb","resolution":{"observed_at":"2026-08-07T11:11:04.458110Z","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":"2026-08-07T11:11:04.448351Z","title":"Conditional deep learning for energy-efficient and enhanced pattern recognition,","venue":null,"work_id":"2fc01f3d-3587-436d-8b3a-004b4a3863e6","year":2016},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.896104Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:123d2f5f6d159465a3d7d119331d9fc7fdb78b0ade41a1f340c9f6c7f5918a8c","observation_id":"26e05c21-834d-4509-aebb-36f3138eb3a3","resolution":{"observed_at":"2026-08-07T11:11:04.450898Z","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":"2026-08-07T11:11:04.440575Z","title":"Why should we add early exits to neural networks?,","venue":null,"work_id":"8c1b2642-06b6-4ffc-a6c9-1c3bc752251e","year":2020},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.898849Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:70c5e5f212cbc5386f8de5c93649ee4e471c031fff41d6de9aeae67759083722","observation_id":"8dd56c5a-77d4-4143-ae6d-a03f35dcb416","resolution":{"observed_at":"2026-08-07T11:11:04.443159Z","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":"2026-08-07T11:11:04.432021Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"a5ab7d0f-f04c-4669-93f4-9b88e6e51578","year":2016},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.901530Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:e761bdb1bd0e19cc5a6d3d8bd1c63ba90b3699e360d0b01421566c42e038e24d","observation_id":"504170b4-bfea-42ef-8af3-b1921cc3ee4d","resolution":{"observed_at":"2026-08-07T11:11:04.435727Z","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":"2026-08-07T11:11:04.423946Z","title":"Multilayer feedfor- ward networks are universal approximators,","venue":null,"work_id":"0f5d098b-8616-4c02-a3a3-4a58e5d296cf","year":1989},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.903927Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:a9d2214495cb2890bedd8717768c551887e4a070a20c2ce284038ffa59f0da19","observation_id":"942e46d9-5870-4b15-adcb-6f143376006d","resolution":{"observed_at":"2026-08-07T11:11:04.426906Z","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":"2026-08-07T11:11:04.416117Z","title":null,"venue":null,"work_id":"e910db10-abe9-4710-a141-c0c186ab6169","year":1961},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.907047Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:d9ced40dc5255e45ceb7f8cdc11e260adcc2c246dec78f8e8f2d1298b376672e","observation_id":"2eded94b-4b09-404b-9c28-f75f7dafe61b","resolution":{"observed_at":"2026-08-07T11:11:04.418679Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:11:04.400012Z","title":"On functions of three variables,","venue":null,"work_id":"be967992-81ca-4d0b-9a7d-af39e21d100a","year":1957},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.909723Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:e7377309e2093a455f251ca583241c9e9808c0c86c5b5a4d8ae76a5de5916888","observation_id":"1344069c-13ae-464f-b7f8-cf2c14abad90","resolution":{"observed_at":"2026-08-07T11:11:04.406646Z","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":"2026-08-07T11:11:04.381242Z","title":"On the representations of continuous functions of many variables by superposition of continuous functions of one variable and addition,","venue":null,"work_id":"9fcb8c09-6c7f-4d4c-9c09-d769892b7cc3","year":1957},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.912463Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:273a6fcb4912b758d646e6af740a14db5f087ba9679f09e2d6d140237a4e36ed","observation_id":"aebab636-0f9e-4e2f-b55d-1e29e66e7055","resolution":{"observed_at":"2026-08-07T11:11:04.390140Z","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":"2405.06721","last_updated":"2024-05-10T06:03:45Z","snapshot_observed_at":"2026-08-04T21:06:56.482139Z","submitted_at":"2024-05-10T06:03:45Z","title":"Kolmogorov-Arnold Networks are Radial Basis Function Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06721","snapshot_observed_at":"2026-08-07T11:11:03.915041Z","title":"Kolmogorov–Arnold networks are Radial Basis Function networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.915041Z"},"links":{"cited_paper":"/paper/2405.06721","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:7d74994d6d7b7d086438de5e459c1b9ce97310fd2e696012c8931d9d7c59516d","observation_id":"9ed08fd6-960e-4906-bb1e-e538186cbef0","resolution":{"observed_at":"2026-08-07T11:11:03.915041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01034","last_updated":"2025-08-14T10:41:49Z","snapshot_observed_at":"2026-08-05T02:05:56.574184Z","submitted_at":"2024-06-03T06:36:04Z","title":"Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01034","snapshot_observed_at":"2026-08-07T11:11:03.918121Z","title":"FourierKAN-GCF: Fourier Kolmogorov–Arnold network–an effective and efficient feature transformation for graph collaborative filtering,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.918121Z"},"links":{"cited_paper":"/paper/2406.01034","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:c8dfdb9e6181ae1d82f6b7230b57e807c9b4b80a979926abd51ba8cd539fa1ab","observation_id":"6e65e441-ed16-407b-8c18-bba1e230aa9d","resolution":{"observed_at":"2026-08-07T11:11:03.918121Z","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":"2026-08-07T11:11:04.366993Z","title":"SineKAN: Kolmogorov–Arnold networks using sinusoidal activation func- tions,","venue":null,"work_id":"baa1a631-5500-484b-9965-b8ec3d987cd2","year":2025},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.921681Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:b85b8d20b6c2cb73eb864ed784c19715354d831747daaf3059421fb340879638","observation_id":"9ad37be4-04ed-4daa-8585-b668d69c239d","resolution":{"observed_at":"2026-08-07T11:11:04.370258Z","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":"2405.07200","last_updated":"2024-06-14T15:46:11Z","snapshot_observed_at":"2026-08-08T21:02:58.042224Z","submitted_at":"2024-05-12T07:55:43Z","title":"Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07200","snapshot_observed_at":"2026-08-07T11:11:03.924515Z","title":"Cheby- shev polynomial-based Kolmogorov–Arnold networks: An efficient architecture for nonlinear function approximation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.924515Z"},"links":{"cited_paper":"/paper/2405.07200","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:16e1147c7e8eac1feaf1f7ad09358380ef7afe14329aaf7281917277cc68bc37","observation_id":"a0751777-5e06-4dd5-9409-0b4be46e69a4","resolution":{"observed_at":"2026-08-07T11:11:03.924515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12832","last_updated":"2024-05-27T15:12:55Z","snapshot_observed_at":"2026-08-09T11:22:30.141945Z","submitted_at":"2024-05-21T14:36:16Z","title":"Wav-KAN: Wavelet Kolmogorov-Arnold Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12832","snapshot_observed_at":"2026-08-07T11:11:03.927574Z","title":"Wav-KAN: Wavelet Kolmogorov– Arnold networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.927574Z"},"links":{"cited_paper":"/paper/2405.12832","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:5fb393b9a77cb03b8010947171f6bab2635308208895a2e1ee4695ef2f93a049","observation_id":"51e77322-7eba-492d-8a77-6c44101394db","resolution":{"observed_at":"2026-08-07T11:11:03.927574Z","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":"2026-08-07T11:11:04.355096Z","title":"On the limited memory BFGS method for large scale optimization,","venue":null,"work_id":"afc8afe3-51fd-42c5-a149-2fad95203d1c","year":1989},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.930681Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:677fc07cf7a5b68d6a4b50b6eeda7f8ecc584e86a5ab6f390bea2c4c2de9dce2","observation_id":"98bdef8e-7d07-48e5-ad45-93630334a05a","resolution":{"observed_at":"2026-08-07T11:11:04.359005Z","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":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-07T11:11:03.933945Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.933945Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:cd3a8099a6e8cb83cee12aef34ba33f473c9db93bcd728bbdf15f575e64e398c","observation_id":"b5680a6b-8cd5-4b90-b31d-df6e9fcac213","resolution":{"observed_at":"2026-08-07T11:11:03.933945Z","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":"2026-08-07T11:11:04.346533Z","title":"Bert loses patience: Fast and robust inference with early exit,","venue":null,"work_id":"f9fb7187-ef0c-49f1-8d5c-3be729065deb","year":2020},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.937192Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:2d02b5440f18a8cf41a42b990866e5080b7839620bbc0696b41939832691c9df","observation_id":"68043b85-e019-43fe-a1fa-39e6bc658b78","resolution":{"observed_at":"2026-08-07T11:11:04.349754Z","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":"2026-08-07T11:11:04.338268Z","title":"BERxiT: Early exiting for BERT with better fine-tuning and extension to regression,","venue":null,"work_id":"b81266f6-257b-41e0-bf25-2b4b5f9b52e0","year":2021},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.940503Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:46e5b0f163596065eb89bbc6dae523b41de896c4b7cfe9643223a0c618c639fa","observation_id":"385e5b8e-f6fe-4c1a-ae1f-12556b207e91","resolution":{"observed_at":"2026-08-07T11:11:04.341403Z","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":"2026-08-07T11:11:04.330296Z","title":"Adaptive inference through early-exit networks: Design, challenges and directions,","venue":null,"work_id":"d2f34786-bef2-4627-ba1c-ed5ceddb09ef","year":2021},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.943420Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:c2ea10a31fa1cadf647fa46370dd83da1568f08acb92c1657410ac583fd64d2b","observation_id":"94d2fd23-3c22-4bfe-b48e-4b50da188693","resolution":{"observed_at":"2026-08-07T11:11:04.333226Z","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":"2026-08-07T11:11:04.322411Z","title":"Early-exit deep neural network - a comprehensive survey,","venue":null,"work_id":"34c44c05-aa13-42a0-8061-8f6fdc30dfc1","year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.946073Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:6c2dbaaa7e888e32dd0e1226d2e1711ba2c5d055d00f29876d5bc24551f609d4","observation_id":"1dd4fd41-df39-4e05-a2ff-7d7d164fadec","resolution":{"observed_at":"2026-08-07T11:11:04.325314Z","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":"2026-08-07T11:11:04.315493Z","title":"Densely connected convolutional networks,","venue":null,"work_id":"1efd8885-f413-4566-bb38-c63b9b537073","year":2017},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.949539Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:0e255b0e36162b0ea8c91f336095e49a32b3a329a926fba88bf30e73b9eaf92c","observation_id":"4cf6d31c-d0f2-443c-9a19-949d81af7070","resolution":{"observed_at":"2026-08-07T11:11:04.317923Z","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":"2026-08-07T11:11:04.303495Z","title":"AI Feynman: A physics- inspired method for symbolic regression,","venue":null,"work_id":"e21d8ef2-be13-4ddd-94d7-8e847152db4a","year":2020},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.953311Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:f50d647f28402ac44006c8aa994796271eed09710ce02e6b78e7694c5f85d4fa","observation_id":"06d65c58-dab0-4f3b-9c41-07010dcd5352","resolution":{"observed_at":"2026-08-07T11:11:04.310432Z","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":"2026-08-07T11:11:04.288067Z","title":"AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity,","venue":null,"work_id":"9e8d9462-b594-4e24-a9a4-a01bb5c835c7","year":2020},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.956184Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:dd8554ace8d8832ad369ab04335041de3fdd0cd757230f3b971562526a258885","observation_id":"aaeb8390-a965-4dbb-aeaa-7ff8fc9100b8","resolution":{"observed_at":"2026-08-07T11:11:04.291866Z","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":"2026-08-07T11:11:04.278078Z","title":"Multiple-valued stationary state and its instabil- ity of the transmitted light by a ring cavity system,","venue":null,"work_id":"4d4fff23-7319-4924-a5eb-5f9487605c30","year":1979},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.959091Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:e3515fce002d1fab3b4dc653dde188a9304d25f4e9c2e64ddcc7b7ad0c4649b5","observation_id":"d75531c1-e74d-4025-8833-9dba91627539","resolution":{"observed_at":"2026-08-07T11:11:04.281858Z","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":"2026-08-07T11:11:04.267170Z","title":"Global dynamical behavior of the optical field in a ring cavity,","venue":null,"work_id":"7f1232ab-1049-4263-aff8-62878f3bd937","year":1985},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.962251Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:663804b2e9b45f8e1952a610b5f122fc50e5682c14a02c94f8f6c8aac04801ef","observation_id":"1002d8dd-f733-4bb4-957c-e0ce8e067651","resolution":{"observed_at":"2026-08-07T11:11:04.270974Z","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":"2026-08-07T11:11:04.256122Z","title":"Nonlinear dynamics and population disappearances,","venue":null,"work_id":"1d0f223d-e717-44c9-bbcc-8a6468387fa1","year":1994},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.965310Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:77bdf9eb0c861455b712415c755739f21938a17546f3345b39e58d3c8718813f","observation_id":"5a5edae7-640d-4c32-910e-00a0878257c8","resolution":{"observed_at":"2026-08-07T11:11:04.260579Z","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":"2402.08255","last_updated":"2024-02-13T07:07:37Z","snapshot_observed_at":"2026-08-09T09:32:54.106144Z","submitted_at":"2024-02-13T07:07:37Z","title":"Distal Interference: Exploring the Limits of Model-Based Continual Learning","version":1},"cited_work":{"arxiv_id":"2402.08255","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.08255","snapshot_observed_at":"2026-08-07T11:11:04.084574Z","title":"Distal Interference: Exploring the Limits of Model-Based Continual Learning","venue":"cs.LG","work_id":"fabcc2c5-4860-4dd6-b3b0-3b6ccf9fb6ad","year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.968149Z"},"links":{"cited_paper":"/paper/2402.08255","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:4d66890d34a779ce3e9706c8008e872a5256cb3485a28771b12d6fc08e18bcfe","observation_id":"97c78513-78a8-420a-aaee-d43056fb5b97","resolution":{"observed_at":"2026-08-07T11:11:04.089123Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:11:04.244739Z","title":"Airfoil self-noise and prediction,","venue":null,"work_id":"ecd86481-6308-44e0-8549-da9689e07774","year":1989},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.971258Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:4a6489e83779deb09f7b5a7c345edf115db9beaba08ef4004ab03fe3579830c5","observation_id":"3a146412-c1d8-450f-a830-c8b893fa13d7","resolution":{"observed_at":"2026-08-07T11:11:04.248578Z","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":"2026-08-07T11:11:04.234444Z","title":"Local and global learning methods for predicting power of a combined gas & steam turbine,","venue":null,"work_id":"42fa54ea-09af-410e-96cc-f24765798bad","year":2012},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.974245Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:03ed4a1038c6a632dbab9ce5afc90d6a399cd2e066a550cd040a0a390ee4431f","observation_id":"de75c24a-b017-42a4-98b6-d34321a68c68","resolution":{"observed_at":"2026-08-07T11:11:04.238091Z","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":"2026-08-07T11:11:04.223966Z","title":"Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods,","venue":null,"work_id":"e93eb0b9-17a4-4ed4-9a07-e4e81eb7ee40","year":2014},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.978383Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:a5d4947ec8ad07bb005bb64fd37b02c514e99097363a5b8a394eabc0443ec919","observation_id":"289d3b0c-8fea-4261-ae1b-8254fe77b1c7","resolution":{"observed_at":"2026-08-07T11:11:04.228227Z","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":"2026-08-07T11:11:04.215050Z","title":"A data-driven statistical model for predicting the critical temperature of a superconductor,","venue":null,"work_id":"ebb472fd-5dd0-45e3-ad86-96917aa1a4d4","year":2018},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.982525Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:35b298f1c4fb02ff1b805cae4d53ca8eee375de0f9af157d871157ecf4308645","observation_id":"00f4535d-7a27-4029-a5ce-3d445dfff0d1","resolution":{"observed_at":"2026-08-07T11:11:04.218427Z","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":"2026-08-07T11:11:04.205849Z","title":"MDR SuperCon datasheet ver.240322","venue":null,"work_id":"26feab22-fc1d-4708-8b2d-d440221910d4","year":null},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.986639Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:47d38521715796265f49f544af7f4658aca6f59208ccb41b144904be101b9aa6","observation_id":"3a85051a-a598-4eb7-838c-b131538967d5","resolution":{"observed_at":"2026-08-07T11:11:04.209685Z","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":"2026-08-07T11:11:04.196435Z","title":"The UCI machine learning repository,","venue":null,"work_id":"0bc019f1-880b-41df-a380-a44a991179b7","year":2025},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.990390Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:655d42b4960c625e2e2e7394afefa3c3f2a4cb3835b4d1dc3d88a9326a11b1ae","observation_id":"fbcf101c-116a-4ecb-84a2-ad4164faddfe","resolution":{"observed_at":"2026-08-07T11:11:04.199821Z","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":"2026-08-07T11:11:04.185514Z","title":"Ensemble learning: A survey,","venue":null,"work_id":"0fb208ee-1fbc-42ff-9dca-9d55bd1f6354","year":2018},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.993182Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:031cdc99b453e78dab20b94e8f3e59534092099f5a5c60e2ff5fff37ef7c7469","observation_id":"e20767f5-769a-4e5d-8b6b-6a4a6f52fcff","resolution":{"observed_at":"2026-08-07T11:11:04.188606Z","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":"2026-08-07T11:11:04.174885Z","title":null,"venue":null,"work_id":"9e5ba85c-2a62-4e83-b3f1-39286f52c84a","year":2013},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.996159Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:473664f5d2a12a3455be7ff303fed26d5b9d4c28773c22614a2db61e3965c711","observation_id":"e26ed2e8-3947-45e2-94fd-071c86dd3933","resolution":{"observed_at":"2026-08-07T11:11:04.178603Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:11:04.164472Z","title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges,","venue":null,"work_id":"3210a5cf-e3d7-44f2-9e51-375c16e7dc1f","year":2021},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.999130Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:c3aa177407bb2b9001e3a52b997d245be04881905daee512e23503d088529169","observation_id":"437801d0-adf3-438f-a590-bb4e26e9f10b","resolution":{"observed_at":"2026-08-07T11:11:04.168395Z","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":"2408.02706","last_updated":"2024-08-05T10:38:34Z","snapshot_observed_at":"2026-08-09T11:46:53.993916Z","submitted_at":"2024-08-05T10:38:34Z","title":"Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability","version":1},"cited_work":{"arxiv_id":"2408.02706","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.02706","snapshot_observed_at":"2026-08-07T11:11:04.070570Z","title":"Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability","venue":"cs.LG","work_id":"5edc7cb7-2582-4dd7-94e4-3716f32fd399","year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:04.002076Z"},"links":{"cited_paper":"/paper/2408.02706","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:003b7652e21a412050a5e8033b28d8831a9c38625c4ec3168bebe8914878b629","observation_id":"7da88c25-96a6-4719-950d-576277de856a","resolution":{"observed_at":"2026-08-07T11:11:04.074657Z","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":"2504.15240","last_updated":"2025-04-21T17:14:05Z","snapshot_observed_at":"2026-08-07T16:00:30.914908Z","submitted_at":"2025-04-21T17:14:05Z","title":"Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning","version":1},"cited_work":{"arxiv_id":"2504.15240","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.15240","snapshot_observed_at":"2026-08-07T11:11:04.054740Z","title":"Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning","venue":"cs.LG","work_id":"ceb88cc5-0f46-48f8-9df0-aca6ca087494","year":2025},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:04.005789Z"},"links":{"cited_paper":"/paper/2504.15240","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:a0fad507a3896d24ad080b5d5cedfa893cd682a378df09e1733e0bd788c8edb7","observation_id":"8fe07d64-d00a-4e4e-93d1-bd0e265e4d30","resolution":{"observed_at":"2026-08-07T11:11:04.060284Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:11:04.154373Z","title":"DARTS: Differentiable architecture search,","venue":null,"work_id":"e9769aec-3946-4e28-8f6a-372ac7224756","year":2019},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:04.009557Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:29abb3ce53b78e8adb6f93ca821f4277d269ffaa98c5faaef52291af2e6bc475","observation_id":"10818575-a76b-4cbf-b03d-185ec90eef80","resolution":{"observed_at":"2026-08-07T11:11:04.157665Z","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":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-07T11:11:04.013104Z","title":"PyTorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:04.013104Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:e23f8275ffea9870fe6380be42dea731db33e8752b7a3fc14d7c4e61a94e6fed","observation_id":"1a8d1f55-150f-4610-a6d2-3ae8ba5af3d8","resolution":{"observed_at":"2026-08-07T11:11:04.013104Z","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":"2026-08-07T11:11:04.144085Z","title":"FourierKAN","venue":null,"work_id":"e15848da-0ff7-4fc9-8fff-9518f5f8c334","year":2024},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:04.016511Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:31512ee666872c68152e66a2593b3821fb10df2c187337a6bc01037609432dc0","observation_id":"a6bf706a-2ba8-4db8-913c-d608809319a9","resolution":{"observed_at":"2026-08-07T11:11:04.147300Z","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":"2026-08-07T11:11:04.548177Z","title":null,"venue":null,"work_id":"556b4e88-88fc-435f-9518-4815f1ab92e5","year":null},"citing_paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T11:11:03.836890Z"},"links":{"citing_paper":"/paper/2506.03302"},"observation_digest":"sha256:949c25d3d9c8e32d12646d17fc69a78ae98e14b9ef5694d9cca836830e1b84cd","observation_id":"be778af0-1bb9-4976-b331-5b1bcfcba2f7","resolution":{"observed_at":"2026-08-07T11:11:04.551086Z","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"}}],"paper":{"arxiv_id":"2506.03302","last_updated":"2025-08-21T16:58:06Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T00:03:58.504463Z","submitted_at":"2025-06-03T18:41:30Z","title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony"},"reference_resolution":{"displayed":62,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":3,"verified_fuzzy":49},"total_outbound_references":62},"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 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2506.03302."}