{"as_of":"2026-08-17T21:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1461cfd85d12c0da3b84be04ec84efe93d28996f04f6d2a51e9faa3779be22b1","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:54:43.733181Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2507.02953/citation-record","integrity":"/paper/2507.02953/integrity","json":"/paper/2507.02953/citation-record.json","paper":"/paper/2507.02953"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:54:44.099013Z","title":"Barbara, Ruigang Wang, and Ian R","venue":null,"work_id":"a879989a-2d17-44fa-b21d-ded07aa56002","year":2025},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.259793Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:f70502286d6062d4f85e69226bbd93b3f179d4f27773fe0ad630954ac581c862","observation_id":"68031814-6257-4cd0-b8f0-abf4b0f6c546","resolution":{"observed_at":"2026-08-06T21:54:44.110245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24164","last_updated":"2026-01-08T17:01:05Z","snapshot_observed_at":"2026-08-16T17:53:54.636855Z","submitted_at":"2024-10-31T17:22:30Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.24164","snapshot_observed_at":"2026-08-06T21:54:43.304291Z","title":"π0: A vision-language-action flow model for general robot control","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.304291Z"},"links":{"cited_paper":"/paper/2410.24164","citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:fb1ae7042d8f9895f70040b2b7bc87de9b0a250a0b10cb0663c98877c2841846","observation_id":"ac32c450-c7d8-4fa1-8910-10aee3d22b17","resolution":{"observed_at":"2026-08-06T21:54:43.304291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.07289","last_updated":"2016-02-22T07:02:58Z","snapshot_observed_at":"2026-08-14T22:21:43.095256Z","submitted_at":"2015-11-23T15:58:05Z","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07289","snapshot_observed_at":"2026-08-06T21:54:43.374298Z","title":"Fast and accurate deep network learning by exponential linear units (elus)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.374298Z"},"links":{"cited_paper":"/paper/1511.07289","citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:5359df1db88fa829520e9c1bdb1a4eb88911989c03f175818145133eddbde99f","observation_id":"b5cadf90-4cdd-4e55-a4e8-704910adf056","resolution":{"observed_at":"2026-08-06T21:54:43.374298Z","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":"2026-08-06T21:54:44.076392Z","title":"Measure theory and fine properties of functions","venue":null,"work_id":"e1c6621c-4ebf-4b26-96b0-38aeccaaca17","year":2018},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.450200Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:d92c4112e912596701af026003fc860dd498691a2cbb70ec526818f8f5bd471a","observation_id":"cb30de8c-9d57-42ee-b7da-127e8aef4638","resolution":{"observed_at":"2026-08-06T21:54:44.084494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T21:54:43.540799Z","title":"Sparsegpt: Massive language models can be accurately pruned in one-shot","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.540799Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:43b145f1176448f6734a14e729ac74468beb04952ea95d1e55a50f3980c1ac91","observation_id":"8939fc97-3c1d-4aa8-8338-73c370226091","resolution":{"observed_at":"2026-08-06T21:54:43.540799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10330","last_updated":"2024-05-24T22:33:04Z","snapshot_observed_at":"2026-08-16T16:58:50.532049Z","submitted_at":"2022-05-20T17:42:38Z","title":"A Review of Safe Reinforcement Learning: Methods, Theory and Applications","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10330","snapshot_observed_at":"2026-08-06T21:54:43.614688Z","title":"A review of safe reinforcement learning: Methods, theory and applications","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.614688Z"},"links":{"cited_paper":"/paper/2205.10330","citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:1c9d4fd3308566fc3d4a07f53cf2ac5ca57f5670f681ff51528f63e1943e76e3","observation_id":"11fa7e59-32c5-40c5-ba11-5618b2dcd1be","resolution":{"observed_at":"2026-08-06T21:54:43.614688Z","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":"2026-08-06T21:54:43.636450Z","title":"Second order derivatives for network pruning: Optimal brain surgeon","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.636450Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:197f77d65ef670666d5b95db2c39d459fa7da1976f177e281213fdcdba82f184","observation_id":"ad9ef977-e278-477b-8f12-8e98788816a8","resolution":{"observed_at":"2026-08-06T21:54:43.636450Z","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":"2026-08-06T21:54:43.646080Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.646080Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:0a4ba27b7c6366a3fd71e2dffb2baffbe50f919ebc372aeb43cb2044b7402c1e","observation_id":"acc74348-4943-4f50-8ba2-8de514599c98","resolution":{"observed_at":"2026-08-06T21:54:43.646080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.08415","last_updated":"2023-06-06T01:53:32Z","snapshot_observed_at":"2026-08-13T19:48:28.322536Z","submitted_at":"2016-06-27T19:20:40Z","title":"Gaussian Error Linear Units (GELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.08415","snapshot_observed_at":"2026-08-06T21:54:43.653791Z","title":"Gaussian error linear units (gelus)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.653791Z"},"links":{"cited_paper":"/paper/1606.08415","citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:e9bfcedca3215c828b7318def44bdf75b182c053604c6e14105991b15579e58a","observation_id":"fe2b7153-c516-482d-ab47-65bd43170578","resolution":{"observed_at":"2026-08-06T21:54:43.653791Z","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":"2026-08-06T21:54:43.666564Z","title":"Optimal brain damage","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.666564Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:9b14dfc559cc6135cedb546a945749519d53a84a4ad855d57fe51bade7582b12","observation_id":"1a2624cb-419a-43aa-b36d-e2d5b0f8ba39","resolution":{"observed_at":"2026-08-06T21:54:43.666564Z","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":"2026-08-06T21:54:43.692854Z","title":"Rectifier nonlinearities improve neural network acoustic models","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.692854Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:83c3bd22ce4f06fb852e6798c30298cfd48043e6b803b7abdf440178e3f1d7b4","observation_id":"621c0a69-4451-4555-82f8-7b6d1f3ac046","resolution":{"observed_at":"2026-08-06T21:54:43.692854Z","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":"2026-08-06T21:54:43.700268Z","title":"Human-level control through deep reinforcement learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.700268Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:91705f657497c5cb0ffbc69d44b9d690e3041deb3921f56d96b7a03eb439f1ed","observation_id":"c052c6bb-254c-4bb8-8b2a-25de66eb08ff","resolution":{"observed_at":"2026-08-06T21:54:43.700268Z","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":"2026-08-06T21:54:43.974326Z","title":"On the effects of pruning on evolved neural controllers for soft robots","venue":null,"work_id":"5ca4a59e-ac48-4a60-b79a-502301d5d350","year":2021},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.711636Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:583ef3142bfaf8a3ea1a993ac329e5e57c2e01cc7c8cbebae73e479b38b2f9ce","observation_id":"04bd743e-85f8-4a15-8f42-b33d1faaa8e6","resolution":{"observed_at":"2026-08-06T21:54:43.986890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T21:54:43.950006Z","title":"Rectified linear units improve restricted boltzmann machines","venue":null,"work_id":"97f24c40-fe03-4ae2-9345-d275eaea5a82","year":2010},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.719065Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:a2782507909997f807a064efa5ba4c7cad85f191dd10d80af20224b3b012937b","observation_id":"e25dfab3-e6f5-4fd7-ab97-8275632d26e3","resolution":{"observed_at":"2026-08-06T21:54:43.958730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T21:54:43.927385Z","title":"Lipschitz regularity of deep neural networks: Analysis and efficient estimation","venue":null,"work_id":"e138e9e9-d25f-4ec0-816a-844c3ebaedf0","year":2018},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.726294Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:0cf24568fef265588bc3c42e595f989f7a5793f41bdd7f71b03fa4b1a506d42b","observation_id":"061ac16d-3a26-4342-9616-28399e032014","resolution":{"observed_at":"2026-08-06T21:54:43.934786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T21:54:43.901417Z","title":"Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective, October 2022","venue":null,"work_id":"195a4404-28de-4dd0-a5ff-0a3418596be7","year":2022},"citing_paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:54:43.733181Z"},"links":{"citing_paper":"/paper/2507.02953"},"observation_digest":"sha256:d4637d69457c396c7f9164769168ba83870f0d2efc8aa7c1a6596034e7b9d382","observation_id":"779f8b3e-3901-4446-8aba-d512216052b9","resolution":{"observed_at":"2026-08-06T21:54:43.911517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.02953","last_updated":"2025-06-29T16:55:17Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-13T10:45:02.969611Z","submitted_at":"2025-06-29T16:55:17Z","title":"Closed-Form Robustness Bounds for Second-Order Pruning of Neural Controller Policies"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":6},"total_outbound_references":16},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.02953."}