{"as_of":"2026-08-13T02:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f304fbca107d2b6ac15eeab6f364afb3327d1c16190e18c25f1da37807ebaee7","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:52:22.018444Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2411.19819/citation-record","integrity":"/paper/2411.19819/integrity","json":"/paper/2411.19819/citation-record.json","paper":"/paper/2411.19819"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2101.08134","last_updated":"2021-03-19T10:43:12Z","snapshot_observed_at":"2026-08-10T05:46:12.625488Z","submitted_at":"2021-01-20T13:59:52Z","title":"Zero-Cost Proxies for Lightweight NAS","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.08134","snapshot_observed_at":"2026-08-12T05:52:21.712749Z","title":"arXiv preprint arXiv:2101.08134 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.712749Z"},"links":{"cited_paper":"/paper/2101.08134","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:aa7506b2134a21d15340d6467415060d01b4c9ddcb4a8713785259f0adad782e","observation_id":"201101fb-e94f-4ad2-840b-068fb4163de8","resolution":{"observed_at":"2026-08-12T05:52:21.712749Z","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":"2026-08-12T05:52:23.022723Z","title":null,"venue":null,"work_id":"d6c31a26-24fe-423f-9c8d-aeee1173c3ee","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.719230Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:97666b03f9a6244be95da82502ecfe24a6c96a20795da300e3c8a78c9d795812","observation_id":"08904327-bc0c-4cbc-85cd-17a140c78835","resolution":{"observed_at":"2026-08-12T05:52:23.027929Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:23.006298Z","title":"cam- bridge university press (1999)","venue":null,"work_id":"5faca5cb-fbcd-4464-a035-44ceb6320aeb","year":1999},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.724849Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:c7a429452abf11ea278a4993f7f3b16c5a4b16225a510f20b311dc2f3b113b92","observation_id":"5514cbd5-7f33-4028-b091-dbf86023a164","resolution":{"observed_at":"2026-08-12T05:52:23.011502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01619","last_updated":"2020-02-14T22:55:51Z","snapshot_observed_at":"2026-08-10T22:02:36.110721Z","submitted_at":"2019-10-03T17:38:10Z","title":"Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01619","snapshot_observed_at":"2026-08-12T05:52:21.730446Z","title":"arXiv preprint arXiv:1910.01619 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.730446Z"},"links":{"cited_paper":"/paper/1910.01619","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:a377c00487eb45ea898693dd07adb345ce4009a6c88f74a36622c81df5df84fd","observation_id":"d3f0700f-2f5a-4059-a7e3-73de9cefd225","resolution":{"observed_at":"2026-08-12T05:52:21.730446Z","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":"2026-08-12T05:52:22.989827Z","title":"In: International Conference on Machine Learning","venue":null,"work_id":"172efa08-4a0a-4e7f-8969-a3d906c8c657","year":null},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.736535Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:397f0905e4b1bfb885308e87eaf7788dc8a436b165b9b2c42335c6159b9c8441","observation_id":"55305073-a017-414d-970d-1310956c2709","resolution":{"observed_at":"2026-08-12T05:52:22.995061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.973704Z","title":"Advances in neural information processing systems9 (1996)","venue":null,"work_id":"76b9da22-89f3-48d5-bffc-64bf45d0e6b4","year":1996},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.742091Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:d6adee902cab7706ba803371020dd7214f43e8b7d98dd6cd035178bee9b9fc1d","observation_id":"79357f2e-5e82-409e-9d02-7480bb44fc28","resolution":{"observed_at":"2026-08-12T05:52:22.978791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.957516Z","title":"Advances in neural information processing systems30 (2017)","venue":null,"work_id":"b5670712-a8ea-48d1-af54-513619d00122","year":2017},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.747570Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:d18898df3c111596ecfc584bffba90a352da4a63490ee0295f43eb267109c3c6","observation_id":"ffd5104a-ab00-4057-bcea-473bb7c37ed9","resolution":{"observed_at":"2026-08-12T05:52:22.962709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.941514Z","title":"In: International Conference on Ma- chine Learning","venue":null,"work_id":"569dedf1-334b-43ff-a536-eb97eabd6ebe","year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.753440Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:3dfc6b1933dee377082e471b1c2f02387ebc5989cccbe8e1dd0c58f17ff1a56b","observation_id":"c4e1bfdd-3686-4d40-9efb-89d0b94aa21b","resolution":{"observed_at":"2026-08-12T05:52:22.946440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.925613Z","title":null,"venue":null,"work_id":"2ebc3542-19c1-472e-aef0-f002d8438510","year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.758607Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:13a625f5f01b023121daf83b6554a1e53fb19a2fe2df5c4d328bd3d06f712afa","observation_id":"6de3f2aa-e1a8-47ea-8f2e-dd295116f87d","resolution":{"observed_at":"2026-08-12T05:52:22.930679Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.00420","last_updated":"2019-01-09T11:11:47Z","snapshot_observed_at":"2026-08-10T10:53:37.637246Z","submitted_at":"2018-12-02T16:39:19Z","title":"Efficient Lifelong Learning with A-GEM","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.00420","snapshot_observed_at":"2026-08-12T05:52:21.763761Z","title":"arXiv preprint arXiv:1812.00420 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.763761Z"},"links":{"cited_paper":"/paper/1812.00420","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:b3f3a847a8c9b2a2ff4b529b6da5604ec582c3da6857cdc462a013b430b3931f","observation_id":"deaecedb-3377-47a3-9ce7-19683aecc498","resolution":{"observed_at":"2026-08-12T05:52:21.763761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.11535","last_updated":"2021-03-16T00:59:19Z","snapshot_observed_at":"2026-08-09T09:07:52.948334Z","submitted_at":"2021-02-23T07:50:44Z","title":"Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.11535","snapshot_observed_at":"2026-08-12T05:52:21.769309Z","title":"arXiv preprint arXiv:2102.11535 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.769309Z"},"links":{"cited_paper":"/paper/2102.11535","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:f309182c7b6c0e84731c6aec52b176cfb8a8774f9f1b142aec1bc7c298be8288","observation_id":"8c6fc3bf-24fe-4a63-b641-426534b591e5","resolution":{"observed_at":"2026-08-12T05:52:21.769309Z","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":"2026-08-12T05:52:22.909422Z","title":null,"venue":null,"work_id":"7cfb174f-a025-483f-9ae6-8272307ec856","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.775019Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:13e36cc8535bd0f6cdda9cd21816ae08a893d12c2c2afa757a481d1e548335cb","observation_id":"a23209a9-7c09-4f96-af2b-0a500cf78a92","resolution":{"observed_at":"2026-08-12T05:52:22.914870Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.892679Z","title":"Mathe- matics of control, signals and systems2(4), 303–314 (1989)","venue":null,"work_id":"50756449-4102-4281-9df3-7fc103d63b03","year":1989},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.781161Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:0d69e44cc3a001acba857e18ac321ee551772c49d2ddb0b49f78b0fd8b63e7f8","observation_id":"ea1e2047-a431-4a62-97f5-55eaf6b91ffc","resolution":{"observed_at":"2026-08-12T05:52:22.897997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-12T05:52:21.786879Z","title":"arXiv preprint arXiv:2001.00326 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.786879Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:edc4e75a75ae45172d7762a256551ca44bfa2a72bf474db8f8562d1e7e582e08","observation_id":"f8602af1-88ed-4ad4-a56a-4d8fd7fa6435","resolution":{"observed_at":"2026-08-12T05:52:21.786879Z","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":"2026-08-12T05:52:22.875472Z","title":null,"venue":null,"work_id":"9feec1a5-3724-437f-b0e1-f07f94dd318c","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.792753Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:0d197808fe158aa666b40497229b42680e30dca75cfdc5e7156be41ffb62bc05","observation_id":"ba7e8bf9-833a-4f3b-9c54-bef432a5d0b4","resolution":{"observed_at":"2026-08-12T05:52:22.880894Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.857347Z","title":"Advances in Neural Information Processing Systems33, 1023–1035 (2020)","venue":null,"work_id":"c512f2e1-0815-4cb4-bdb6-fb5e2f89761d","year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.797834Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:a682a974b1408d77219fcab49e9b88a0b36911254d8b982801d131ae3e9eaba9","observation_id":"e43afa34-4c81-45cd-b61a-46bfd55de760","resolution":{"observed_at":"2026-08-12T05:52:22.862681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.840502Z","title":"In: European conference on computer vision","venue":null,"work_id":"03333ba3-d943-4c4d-9652-412ba79a32cd","year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.802946Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:c4711afba703420479c8a3ca6f2808734cba74005a8c013eb4b6d22c8f15ac7a","observation_id":"7bdd2348-dfda-421a-aea8-3964b2401bd2","resolution":{"observed_at":"2026-08-12T05:52:22.845757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.823770Z","title":"In: Interna- tional Conference on Machine Learning","venue":null,"work_id":"8a578e4e-4740-4188-90a2-e60aa97725aa","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.808074Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:acccd0b591e23c1273f7f47af8c1b1241d7ca4777dfd8f8fde8f233949441cd3","observation_id":"e9f0df1c-18df-4e12-9b09-e3385147621a","resolution":{"observed_at":"2026-08-12T05:52:22.829190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.807785Z","title":"In: Conference on learning theory","venue":null,"work_id":"32ce6e7f-20f4-47b6-852a-e48e1fe7a89f","year":2017},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.813406Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:00d8a3076a11b301da7dd84beeb3eafde13eed27f88c44bbcf77fb3b565fac93","observation_id":"683b663d-d4cf-4836-a055-507fbd52d31d","resolution":{"observed_at":"2026-08-12T05:52:22.812713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.792130Z","title":null,"venue":null,"work_id":"a8657fc0-27f6-4dd0-8563-99069efbde6d","year":2016},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.818700Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:0e07efd091dccee87abd4af43b7e5759e3572134dd0898a8f2f0f1edbc75c4fb","observation_id":"3e1d444d-a24b-449f-ad7a-28bea02bb0bb","resolution":{"observed_at":"2026-08-12T05:52:22.797125Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:21.823944Z","title":"In: International conference on machine learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.823944Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:bf1a6d5c4c7c4664e2592d48d7ecd062a12b4aa6af3dd9084bb94f9a0432dc3a","observation_id":"6c5b55fb-d738-4f46-92b8-915f8308dd01","resolution":{"observed_at":"2026-08-12T05:52:21.823944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:52:21.828796Z","title":"Advances in neural information processing systems 31 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.828796Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:c734a48558eb752126759cbedb90e44cff48c64eafa059f6fb63ab0dd6f86c9b","observation_id":"a8491ba9-8f14-485b-8fee-1135979b85e6","resolution":{"observed_at":"2026-08-12T05:52:21.828796Z","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":"2026-08-12T05:52:22.752867Z","title":"Biometrika 30(1/2), 81–93 (1938)","venue":null,"work_id":"095833ad-0e6a-4dc7-94bc-70bd8c74cda0","year":1938},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.833711Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:92f853ce4f11446a283f01a13c149f0ce9510edb2320b5f054641adff4bd07fe","observation_id":"e9f57309-f039-4aff-9755-7581fbf312b2","resolution":{"observed_at":"2026-08-12T05:52:22.758324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.736008Z","title":"NeurIPS (2019)","venue":null,"work_id":"13c4b08e-8b7d-4866-b0f8-6cf8c6ad482f","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.838978Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:63cccbfe0442b5c0d7a90de70019c0c4f220f3a62dfe2ef5caec49224d80d7c2","observation_id":"0d6458e4-8a86-4a16-89e3-397cf33fe8ff","resolution":{"observed_at":"2026-08-12T05:52:22.740942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.02340","last_updated":"2019-02-23T07:45:29Z","snapshot_observed_at":"2026-07-06T07:06:10.289386Z","submitted_at":"2018-10-04T17:39:58Z","title":"SNIP: Single-shot Network Pruning based on Connection Sensitivity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.02340","snapshot_observed_at":"2026-08-12T05:52:21.844113Z","title":"arXiv preprint arXiv:1810.02340 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.844113Z"},"links":{"cited_paper":"/paper/1810.02340","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:21d0402bcafb1c27bf5c74d24a136496f1eed6128f4edb6e9a66a6c69b60656c","observation_id":"444d9231-082c-4b01-958e-6ae7c92e0ccf","resolution":{"observed_at":"2026-08-12T05:52:21.844113Z","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":"2026-08-12T05:52:22.719391Z","title":"In: Uncertainty in artificial intelligence","venue":null,"work_id":"46521944-858a-4220-9963-ac051e5421be","year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.849854Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:586db2093003a9c4f2342730a1516298dc3751b11dc11808516cc123e1f3d165","observation_id":"f435dc0c-5a3c-4797-83ff-71ad44e24382","resolution":{"observed_at":"2026-08-12T05:52:22.724501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.702386Z","title":"In: Conference on learning theory","venue":null,"work_id":"53d4523c-31c4-4432-8411-6885432bf874","year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.854847Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:0537bf68d10d255a8cac8e1e564473d8e85f4b607a48347b1424fafb5b8e772d","observation_id":"2f2393b0-5a45-406f-b8d9-fee952df4d70","resolution":{"observed_at":"2026-08-12T05:52:22.707731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.686137Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision","venue":null,"work_id":"2f98c471-bf87-4ab7-9ad2-05d3ec8f6958","year":2021},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.859882Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:612740fa5a14acb9edd1223f3cd384e72babbea6bd4826281e6ce4138be154b5","observation_id":"d32ae87d-baf4-4f30-8a62-d09a9017b1eb","resolution":{"observed_at":"2026-08-12T05:52:22.691238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.669574Z","title":null,"venue":null,"work_id":"9feb6921-d29f-4469-a59a-1f53abd42962","year":2017},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.865126Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:d3529ff28156d271ea157bfa3be4f3fc8a77b1c9ee4317aabe5f2c9e76600f43","observation_id":"1d4ad9fb-2881-4bf4-8745-d0a90e27cf17","resolution":{"observed_at":"2026-08-12T05:52:22.674466Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.09055","last_updated":"2019-04-23T06:29:32Z","snapshot_observed_at":"2026-08-09T13:23:20.015254Z","submitted_at":"2018-06-24T00:06:13Z","title":"DARTS: Differentiable Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.09055","snapshot_observed_at":"2026-08-12T05:52:21.870059Z","title":"arXiv preprint arXiv:1806.09055 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.870059Z"},"links":{"cited_paper":"/paper/1806.09055","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:a657d88fd324f9115ba0d0a66b612372891d568a2500581dc7931a6ff37342c4","observation_id":"e63d6ad6-1cc3-4cf1-be8d-adec8a79e413","resolution":{"observed_at":"2026-08-12T05:52:21.870059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:52:21.874965Z","title":"Advances in neural information processing systems30 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.874965Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:ca881e1d18e1cd4654ce13d59afcdc9d9e224c3711a4417f30e451032fcc741d","observation_id":"e0246b1f-4c94-45fa-8753-f52ec989a00a","resolution":{"observed_at":"2026-08-12T05:52:21.874965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.09640","last_updated":"2020-02-23T13:08:53Z","snapshot_observed_at":"2026-07-06T08:45:55.551013Z","submitted_at":"2019-12-20T04:42:43Z","title":"AtomNAS: Fine-Grained End-to-End Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"1912.09640","doi":null,"metadata_source":"pith","pith_arxiv_id":"1912.09640","snapshot_observed_at":"2026-08-12T05:52:22.199976Z","title":"AtomNAS: Fine-Grained End-to-End Neural Architecture Search","venue":"cs.CV","work_id":"bbd6df41-df16-45fa-ab3d-4cf2aaa0fb05","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.879964Z"},"links":{"cited_paper":"/paper/1912.09640","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:ca94715e7c21957a2712c57beec9abadfceda62593527a8249da983496e472aa","observation_id":"7acf7796-d12c-4f1a-8ec9-71fe554e11f8","resolution":{"observed_at":"2026-08-12T05:52:22.208387Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.642065Z","title":"In: ICML","venue":null,"work_id":"039ae12a-fb04-40a7-9531-0da481b393d1","year":2021},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.885254Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:78dd5b954b8e30902818c90b86b7c9cdc6678eaefdd3da0003c9be3f1c92091b","observation_id":"233d56f5-e4f4-457d-8267-8d53375ca15e","resolution":{"observed_at":"2026-08-12T05:52:22.646724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.626356Z","title":"Advances in neural information processing systems27 (2014)","venue":null,"work_id":"4881dcc6-d0b0-45c2-bfc5-c27428a4dff4","year":2014},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.890360Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:3ed82f3be32612ed73ed7a1310fefe817ef3e3218b09d441efefd5f7867f2a37","observation_id":"5c31383c-de15-4da8-806e-d529e5c6f4d4","resolution":{"observed_at":"2026-08-12T05:52:22.631530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.610489Z","title":"Advances in neural information processing systems30 (2017)","venue":null,"work_id":"f2b21a2d-afab-4ccf-aa86-b20095d4c283","year":2017},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.895218Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:ca5122559c7d336f71c0152950fd4b4b7cc94c57e5c6d725f058042e537ab260","observation_id":"1cda98b8-b464-4f41-8821-2e9b564258af","resolution":{"observed_at":"2026-08-12T05:52:22.615530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.594234Z","title":"In: Conference on learning theory","venue":null,"work_id":"f891a0b4-b715-4475-b216-fddcb61a9668","year":2015},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.900337Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:f51131ab5acc0a7689d5d1eafb4e5162da4018a1d012d1eb23d71499f329d6f9","observation_id":"bf686d33-43c3-426e-a3d8-a2c2bb15c338","resolution":{"observed_at":"2026-08-12T05:52:22.599130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.578628Z","title":null,"venue":null,"work_id":"f184860f-d938-4e00-bc47-1948e895b8d4","year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.905942Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:71cb89229e5740086178b9c83b00183202fd4f19f09596c3b2441328c6bc9d52","observation_id":"60768418-f2ed-4b8a-a685-c90604327ccd","resolution":{"observed_at":"2026-08-12T05:52:22.583492Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.561590Z","title":null,"venue":null,"work_id":"4b26b91d-7207-4e1b-a1de-10c0d9bfbd70","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.910997Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:1d626a7322be0a9e571c19459970d78509064a6d89db9074dd1f913996a07045","observation_id":"51f890e3-5206-4b35-8923-9994a33e78ca","resolution":{"observed_at":"2026-08-12T05:52:22.566634Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.545868Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":"14c8be50-7a81-41a1-9740-24bebdea1d76","year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.915895Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:f2872b4e51e30369d5ffdd19f6271ce2cd05c4ecd838766e7da54d1753c90d58","observation_id":"a07cbe34-c546-4f23-bfd3-a752df54993b","resolution":{"observed_at":"2026-08-12T05:52:22.550877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.530058Z","title":null,"venue":null,"work_id":"5386110c-113c-447c-a450-2f7c5689052b","year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.920959Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:96efd577f1940fa6e9d392149662bb2d28188746ce0dc890c6c361f9f34a31ad","observation_id":"c6f761e6-e49a-43c3-9db6-566804eacdd1","resolution":{"observed_at":"2026-08-12T05:52:22.535328Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.512659Z","title":"In: International Conference on Medical image computing and computer-assisted intervention","venue":null,"work_id":"f87ddd89-712f-433b-9d01-170abaadb20d","year":2015},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.925827Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:a64124e171f6c5f58bf590c332e7992850ae901924a14d6e9daeed8c27671e68","observation_id":"3b794e29-f381-4915-86e4-69c282070283","resolution":{"observed_at":"2026-08-12T05:52:22.518133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.496197Z","title":null,"venue":null,"work_id":"c7d3bbbc-c16f-4804-a692-ca38dc6b035f","year":null},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.930939Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:2f34714ce33058a088713ae362ee0a33cade5aa5704bd19d57153410a80e8588","observation_id":"be0f6b11-3727-4ef9-87c8-5a50d663090f","resolution":{"observed_at":"2026-08-12T05:52:22.501119Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.479212Z","title":"In: International Conference on Machine Learning","venue":null,"work_id":"d1c2281c-c01a-4aff-b07a-80ba264babf7","year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.936242Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:ed5525756ce1160ca28e67c9f66207b855f18ab7ff48842d7936007b1b8ba07f","observation_id":"dc8de3cf-3d8a-4f2c-924d-3f729ade9559","resolution":{"observed_at":"2026-08-12T05:52:22.484188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.463785Z","title":null,"venue":null,"work_id":"a0984c4e-9ccb-4fe7-9aeb-d004ebfa2ff1","year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.941155Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:19f735bb019e9f3afaac55a455dffb8499b3541aa60e9621cc15a75c4ffc0419","observation_id":"958ee5ad-cead-43c7-883a-ce0917baba18","resolution":{"observed_at":"2026-08-12T05:52:22.468565Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.447566Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":"24ae2b43-6c79-40fb-9d5d-ed55e7e5408a","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.946329Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:5e0d61e025783fcc9ae0b3471d169564956a324dca9ae85f5aaa69481435180f","observation_id":"9bec1379-655b-4044-80ff-b264f7c80ea3","resolution":{"observed_at":"2026-08-12T05:52:22.452963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.428678Z","title":"Advances in neural information processing systems 33, 6377–6389 (2020)","venue":null,"work_id":"b8b7a932-06f9-4d01-bb5e-3aa63cb63949","year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.951387Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:f512a2f293477eadeaebbf62aad0b607f7c7e25743af536e7113b5d471658e22","observation_id":"d9af80a8-2692-4db4-b66c-df9a5afc315d","resolution":{"observed_at":"2026-08-12T05:52:22.434524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.08101","last_updated":"2015-09-29T13:44:37Z","snapshot_observed_at":"2026-08-03T18:23:00.117102Z","submitted_at":"2015-09-27T15:26:58Z","title":"Representation Benefits of Deep Feedforward Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.08101","snapshot_observed_at":"2026-08-12T05:52:21.956430Z","title":"arXiv preprint arXiv:1509.08101 (2015)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.956430Z"},"links":{"cited_paper":"/paper/1509.08101","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:2f6167351e3efd7791f34c4ba6ad7f09ed13019c43974383310ea525dc17f4e7","observation_id":"b75f4a21-23f1-4bd3-b15b-32412ddeeb33","resolution":{"observed_at":"2026-08-12T05:52:21.956430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.05787","last_updated":"2018-07-09T10:38:35Z","snapshot_observed_at":"2026-08-10T14:26:53.680117Z","submitted_at":"2018-01-17T18:34:33Z","title":"Faster gaze prediction with dense networks and Fisher pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.05787","snapshot_observed_at":"2026-08-12T05:52:21.961840Z","title":"arXiv preprint arXiv:1801.05787 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.961840Z"},"links":{"cited_paper":"/paper/1801.05787","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:0f9e51a32e12fa819db6b221316881348e498fe9cc557ae44dcb87891ba8bd28","observation_id":"2b941cf8-c091-4792-aea3-337bd45b52e6","resolution":{"observed_at":"2026-08-12T05:52:21.961840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.07376","last_updated":"2020-08-07T00:02:33Z","snapshot_observed_at":"2026-08-10T04:53:00.900945Z","submitted_at":"2020-02-18T05:14:47Z","title":"Picking Winning Tickets Before Training by Preserving Gradient Flow","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.07376","snapshot_observed_at":"2026-08-12T05:52:21.966912Z","title":"arXiv preprint arXiv:2002.07376 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.966912Z"},"links":{"cited_paper":"/paper/2002.07376","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:9604390e3dfd0eb80e2be073a3255e6e73b77a7c15377f04e7fa4b7a4bd0891c","observation_id":"583b95d5-276b-4096-abb8-1bac657b6c8c","resolution":{"observed_at":"2026-08-12T05:52:21.966912Z","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":"2026-08-12T05:52:22.410439Z","title":"In: ICLR Blog Track (2022), https: //iclr-blog-track.github.io/2022/03/25/zero-cost-proxies/ , https://iclr- blog-track.github.io/2022/03/25/zero-cost-proxies/","venue":null,"work_id":"7f5c842a-5338-4abf-bbac-fb1b3d940e40","year":2022},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.972217Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:17b4cab59d54fe057acc969d2178f0021db0517661eae21c637d148c94de0f12","observation_id":"cdeba43a-f9fd-421f-b178-85ed31627a22","resolution":{"observed_at":"2026-08-12T05:52:22.415629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.393293Z","title":"In: Proceedings of the IEEE/CVF International Con- ference on Computer Vision","venue":null,"work_id":"c7ef70d9-da35-4362-988e-842400853ab3","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.977220Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:14757f56240c040c6c8d6235a6de336df0d1db1e0933a57224399727301fbf54","observation_id":"42769852-62dd-45f1-9ba6-5c265086fe99","resolution":{"observed_at":"2026-08-12T05:52:22.399114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.05737","last_updated":"2020-04-07T06:20:35Z","snapshot_observed_at":"2026-08-04T15:53:57.103153Z","submitted_at":"2019-07-12T13:26:09Z","title":"PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.05737","snapshot_observed_at":"2026-08-12T05:52:21.982220Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.982220Z"},"links":{"cited_paper":"/paper/1907.05737","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:2394668272181706b77724445088eae90397d3fc4144570a6c7c4b67ea03b1a6","observation_id":"8445eca8-3403-4e8d-a322-d283640666cc","resolution":{"observed_at":"2026-08-12T05:52:21.982220Z","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":"2026-08-12T05:52:22.376422Z","title":"Advances in neural information processing systems30 (2017)","venue":null,"work_id":"ab1f12af-ea98-491b-84c9-8713419b605f","year":2017},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.987477Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:e692b3210e0e24c22fd245b74f311e08f6353efdc41cebc614d2498cfc281018","observation_id":"353e31bc-de58-484b-aedb-9481bf17cccb","resolution":{"observed_at":"2026-08-12T05:52:22.381810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T05:52:22.359731Z","title":"In: International conference on machine learning","venue":null,"work_id":"4df87f57-3214-4809-a86a-f526cac9e901","year":2019},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.992588Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:f8c3213347c9040f45551d2f478bd53899ec2f8f8572af4247fb26790a47ee27","observation_id":"63c9314d-a1a3-4ee3-8be8-b4e6e064a70d","resolution":{"observed_at":"2026-08-12T05:52:22.365180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.01072","last_updated":"2020-04-28T19:08:06Z","snapshot_observed_at":"2026-08-10T20:14:36.310636Z","submitted_at":"2020-01-04T12:47:58Z","title":"Empirical Studies on the Properties of Linear Regions in Deep Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.01072","snapshot_observed_at":"2026-08-12T05:52:21.997663Z","title":"arXiv preprint arXiv:2001.01072 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.997663Z"},"links":{"cited_paper":"/paper/2001.01072","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:6728cde51a57e7c71c4fcff0d695593d4c91cf18d20a0e70a6ae3c3df4251dc4","observation_id":"4f7e29be-fa00-4974-b1c7-d026d357a396","resolution":{"observed_at":"2026-08-12T05:52:21.997663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.08616","last_updated":"2022-06-18T19:34:43Z","snapshot_observed_at":"2026-08-04T20:30:24.201248Z","submitted_at":"2021-10-16T17:03:10Z","title":"GradSign: Model Performance Inference with Theoretical Insights","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.08616","snapshot_observed_at":"2026-08-12T05:52:22.002819Z","title":"arXiv preprint arXiv:2110.08616 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:22.002819Z"},"links":{"cited_paper":"/paper/2110.08616","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:5c5db0a01ea473fadcc4d9e02dfed05ff403fbb2f466c1ac2cb033fb899be28a","observation_id":"e1095aca-5b84-41fd-b1cc-5cbab081907f","resolution":{"observed_at":"2026-08-12T05:52:22.002819Z","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":"2026-08-12T05:52:22.342965Z","title":null,"venue":null,"work_id":"2d80a85f-d5a1-4add-9bed-f887999cfe3f","year":2018},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:22.008162Z"},"links":{"citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:ad9ab60fa68e73ec09fbd46eadf6c3278f07fd85a677391d4facbd96c28a5a2d","observation_id":"f3e5f462-95c4-4574-87b6-700352c62edc","resolution":{"observed_at":"2026-08-12T05:52:22.348010Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01578","last_updated":"2017-02-15T05:28:05Z","snapshot_observed_at":"2026-07-06T05:17:29.499249Z","submitted_at":"2016-11-05T00:41:37Z","title":"Neural Architecture Search with Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01578","snapshot_observed_at":"2026-08-12T05:52:22.013204Z","title":"arXiv preprint arXiv:1611.01578 (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:22.013204Z"},"links":{"cited_paper":"/paper/1611.01578","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:ca5f375bdc4c527e22bfd8bbebe10ecc1a418956c1b77c9efea51f0f2c45ada7","observation_id":"bef98019-f1b7-47a0-ae90-2769e010f6c3","resolution":{"observed_at":"2026-08-12T05:52:22.013204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11371","last_updated":"2021-08-25T17:58:21Z","snapshot_observed_at":"2026-07-06T11:41:31.540876Z","submitted_at":"2021-08-25T17:58:21Z","title":"Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.11371","snapshot_observed_at":"2026-08-12T05:52:22.018444Z","title":"arXiv preprint arXiv:2108.11371 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:22.018444Z"},"links":{"cited_paper":"/paper/2108.11371","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:252646666cb88419fd60511441c119ae8b01cbd7cede90c786d2dbc9715593ae","observation_id":"05061c4f-2c42-4507-8df7-e7d3da2346f1","resolution":{"observed_at":"2026-08-12T05:52:22.018444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T23:47:04.535585Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":30,"verified_exact":0,"verified_fuzzy":28},"total_outbound_references":59},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2411.19819."}