{"as_of":"2026-08-09T11:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:522aa45faf10d144d825496f116ce7513b1e9054bf69083aadddd4dfa532f834","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:55:08.346813Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":13,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-06T22:55:08.346813Z","title":"Zico Kolter","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02916","last_updated":"2025-06-25T09:27:02Z","snapshot_observed_at":"2026-08-08T14:53:37.272706Z","submitted_at":"2025-06-25T09:27:02Z","title":"Efficient Certified Reasoning for Binarized Neural Networks","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-06T22:55:08.346813Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2507.02916"},"observation_digest":"sha256:059ec999c009e5611ed6bd892246d0c80ee73d2bfe49a51cf92f1fb3ed9d24ff","observation_id":"43fb3c35-88de-4595-8a80-51e73896ffc0","resolution":{"observed_at":"2026-08-06T22:55:08.346813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-06T16:16:29.718627Z","title":"Zico Kolter","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14492","last_updated":"2025-07-19T05:33:57Z","snapshot_observed_at":"2026-08-08T14:51:04.371014Z","submitted_at":"2025-07-19T05:33:57Z","title":"Glitches in Decision Tree Ensemble Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T16:16:29.718627Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2507.14492"},"observation_digest":"sha256:e47782b6c828fc98c0dfe7e6efabe40412755625ce9b6090ca620376309b2096","observation_id":"4f4827b1-be76-4440-b60d-d63b8dc3317c","resolution":{"observed_at":"2026-08-06T16:16:29.718627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-05T16:56:11.805323Z","title":"Zico Kolter","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.17456","last_updated":"2025-09-13T23:06:06Z","snapshot_observed_at":"2026-08-08T11:27:24.534574Z","submitted_at":"2025-08-24T17:19:53Z","title":"Adversarial Examples Are Not Bugs, They Are Superposition","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-05T16:56:11.805323Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2508.17456"},"observation_digest":"sha256:9e8c446fccef208f1daddd1c02af361985bf631d823df61bd2f4df4ae26d7550","observation_id":"cf67916c-72ba-4f89-847c-4e87ed84836f","resolution":{"observed_at":"2026-08-05T16:56:11.805323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-02T19:17:08.753187Z","title":"arXiv preprint arXiv:2103.06624 (2021) IoUCert: Robustness Verification for Anchor-based Object Detectors 19","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.03043","last_updated":"2026-07-20T13:07:52Z","snapshot_observed_at":"2026-08-06T03:32:29.981336Z","submitted_at":"2026-03-03T14:36:46Z","title":"IoUCert: Robustness Verification for Anchor-based Object Detectors","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-02T19:17:08.753187Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2603.03043"},"observation_digest":"sha256:3ade9c7d0c06f93ea34ac4f2f5ccd6e72edf16b829b583c68c40e652bbcd9001","observation_id":"583ef8fc-4b3f-4ae2-9f81-88895e271d9e","resolution":{"observed_at":"2026-08-02T19:17:08.753187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-07-15T14:07:56.936206Z","title":"arXiv preprint arXiv:2103.06624 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.13334","last_updated":"2026-07-19T22:40:37Z","snapshot_observed_at":"2026-08-02T18:47:57.962901Z","submitted_at":"2026-03-06T06:13:24Z","title":"Lipschitz-Based Robustness Certification Under Floating-Point Execution","version":5},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-15T14:07:56.936206Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2603.13334"},"observation_digest":"sha256:e03f824363795e128a7a3ebb87386d3df62a0cef3b1a74aa6dc50e203fb7ac1c","observation_id":"d56c3d69-9a3c-43fd-819a-a3f3486d85d2","resolution":{"observed_at":"2026-07-15T14:07:56.936206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-02T18:47:59.469696Z","title":"arXiv preprint arXiv:2103.06624 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.13334","last_updated":"2026-07-19T22:40:37Z","snapshot_observed_at":"2026-08-02T18:47:57.962901Z","submitted_at":"2026-03-06T06:13:24Z","title":"Lipschitz-Based Robustness Certification Under Floating-Point Execution","version":6},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-02T18:47:59.469696Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2603.13334"},"observation_digest":"sha256:695cb86c7ad76ee009cca18387eb1d9773356e781a35bbc42d7e665f0ded6ca4","observation_id":"9b471d62-434c-458d-8292-6dcf78fd9efb","resolution":{"observed_at":"2026-08-02T18:47:59.469696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":"2103.06624","doi":"10.48550/arxiv.2103.06624","metadata_source":"arxiv_reference","pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zico , year =","venue":"arXiv (Cornell University)","work_id":"80f16d7a-cd00-4dd5-9fb7-974c1319e3fa","year":2021},"citing_paper":{"arxiv_id":"2604.06289","last_updated":"2026-04-07T13:19:09Z","snapshot_observed_at":"2026-07-06T22:54:48.916799Z","submitted_at":"2026-04-07T13:19:09Z","title":"Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T19:50:19.037987Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2604.06289"},"observation_digest":"sha256:784bb9c07d29627ea71bbb2016335a366703c5913cd4142ad284382085149e7b","observation_id":"f35db220-a4eb-42de-b25e-5324593f2002","resolution":{"observed_at":"2026-05-10T19:50:45.551562Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":"2103.06624","doi":"10.48550/arxiv.2103.06624","metadata_source":"arxiv_reference","pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zico , year =","venue":"arXiv (Cornell University)","work_id":"80f16d7a-cd00-4dd5-9fb7-974c1319e3fa","year":2021},"citing_paper":{"arxiv_id":"2605.13845","last_updated":"2026-05-14T17:00:29Z","snapshot_observed_at":"2026-07-06T23:25:21.032938Z","submitted_at":"2026-05-13T17:59:40Z","title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","version":1},"reference_index":124,"source":"arxiv_source","source_observed_at":"2026-05-14T17:29:39.346689Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2605.13845"},"observation_digest":"sha256:ed8f897573982ccf15682ac8160428cff77c4c5afc3b66c2c483a96bf950e688","observation_id":"3bf1a8e9-72f5-42bd-bbf3-d7bff1d04e7e","resolution":{"observed_at":"2026-05-14T17:32:30.387930Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":"2103.06624","doi":"10.48550/arxiv.2103.06624","metadata_source":"arxiv_reference","pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zico , year =","venue":"arXiv (Cornell University)","work_id":"80f16d7a-cd00-4dd5-9fb7-974c1319e3fa","year":2021},"citing_paper":{"arxiv_id":"2605.13845","last_updated":"2026-05-14T17:00:29Z","snapshot_observed_at":"2026-07-06T23:25:21.032938Z","submitted_at":"2026-05-13T17:59:40Z","title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","version":2},"reference_index":124,"source":"arxiv_source","source_observed_at":"2026-05-15T05:40:16.030363Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2605.13845"},"observation_digest":"sha256:ca5e11351fee1353740ab3067e312e3505244eb9ff79a39e495830ef9a37bc45","observation_id":"7cda1b94-f3b6-423f-a908-77f783d2accb","resolution":{"observed_at":"2026-05-15T05:45:05.072638Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification","version":2},"cited_work":{"arxiv_id":"2103.06624","doi":"10.48550/arxiv.2103.06624","metadata_source":"arxiv_reference","pith_arxiv_id":"2103.06624","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zico , year =","venue":"arXiv (Cornell University)","work_id":"80f16d7a-cd00-4dd5-9fb7-974c1319e3fa","year":2021},"citing_paper":{"arxiv_id":"2606.09377","last_updated":"2026-06-09T11:54:13Z","snapshot_observed_at":"2026-08-08T19:28:41.213160Z","submitted_at":"2026-06-08T11:56:29Z","title":"Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-27T17:20:04.247977Z"},"links":{"cited_paper":"/paper/2103.06624","citing_paper":"/paper/2606.09377"},"observation_digest":"sha256:34580f3295d67ea8f52c3ceab372e886bfeac0c0073e0545244c7ae9cf42e669","observation_id":"bdd464b2-7033-4986-8ed9-77a57806aaf4","resolution":{"observed_at":"2026-07-03T00:17:29.226247Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2103.06624/citation-record","integrity":"/paper/2103.06624/integrity","json":"/paper/2103.06624/citation-record.json","paper":"/paper/2103.06624"},"outbound":[],"paper":{"arxiv_id":"2103.06624","last_updated":"2021-10-31T22:51:18Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T14:50:29.644660Z","submitted_at":"2021-03-11T11:56:54Z","title":"Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2103.06624."}