{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:WKH6THR6MWXMFGH6AJMUIZU42G","short_pith_number":"pith:WKH6THR6","schema_version":"1.0","canonical_sha256":"b28fe99e3e65aec298fe025944669cd1bd29c3db9abc3a430b971ada39a15100","source":{"kind":"arxiv","id":"2111.11277","version":1},"attestation_state":"computed","paper":{"title":"BarrierNet: A Safety-Guaranteed Layer for Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Daniela Rus, Ramin Hasani, Wei Xiao, Xiao Li","submitted_at":"2021-11-22T15:38:11Z","abstract_excerpt":"This paper introduces differentiable higher-order control barrier functions (CBF) that are end-to-end trainable together with learning systems. CBFs are usually overly conservative, while guaranteeing safety. Here, we address their conservativeness by softening their definitions using environmental dependencies without loosing safety guarantees, and embed them into differentiable quadratic programs. These novel safety layers, termed a BarrierNet, can be used in conjunction with any neural network-based controller, and can be trained by gradient descent. BarrierNet allows the safety constraints"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2111.11277","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-11-22T15:38:11Z","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"title_canon_sha256":"024fc5b50b908abd6c6b8e957658c99e0abf38342ee254bec5c2be13868c4e77","abstract_canon_sha256":"cf5e7003851b03350bc27d0e63fee094a0b90908f7eaf17b84b5579b0e9c3cdb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:34:01.618762Z","signature_b64":"9AaZMcwYd3vH3/WSS86MA47tn/4TlfgvQlMd3r3MVQ7npFP0QbQm7NQjd0xeX+GUnA01TDKrBk4C0F+p4ur1DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b28fe99e3e65aec298fe025944669cd1bd29c3db9abc3a430b971ada39a15100","last_reissued_at":"2026-07-05T03:34:01.618371Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:34:01.618371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BarrierNet: A Safety-Guaranteed Layer for Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Daniela Rus, Ramin Hasani, Wei Xiao, Xiao Li","submitted_at":"2021-11-22T15:38:11Z","abstract_excerpt":"This paper introduces differentiable higher-order control barrier functions (CBF) that are end-to-end trainable together with learning systems. CBFs are usually overly conservative, while guaranteeing safety. Here, we address their conservativeness by softening their definitions using environmental dependencies without loosing safety guarantees, and embed them into differentiable quadratic programs. These novel safety layers, termed a BarrierNet, can be used in conjunction with any neural network-based controller, and can be trained by gradient descent. BarrierNet allows the safety constraints"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.11277","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.11277/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2111.11277","created_at":"2026-07-05T03:34:01.618425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.11277v1","created_at":"2026-07-05T03:34:01.618425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.11277","created_at":"2026-07-05T03:34:01.618425+00:00"},{"alias_kind":"pith_short_12","alias_value":"WKH6THR6MWXM","created_at":"2026-07-05T03:34:01.618425+00:00"},{"alias_kind":"pith_short_16","alias_value":"WKH6THR6MWXMFGH6","created_at":"2026-07-05T03:34:01.618425+00:00"},{"alias_kind":"pith_short_8","alias_value":"WKH6THR6","created_at":"2026-07-05T03:34:01.618425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.11755","citing_title":"Reachability Barrier Networks: Learning Hamilton-Jacobi Solutions for Smooth and Flexible Control Barrier Functions","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G","json":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G.json","graph_json":"https://pith.science/api/pith-number/WKH6THR6MWXMFGH6AJMUIZU42G/graph.json","events_json":"https://pith.science/api/pith-number/WKH6THR6MWXMFGH6AJMUIZU42G/events.json","paper":"https://pith.science/paper/WKH6THR6"},"agent_actions":{"view_html":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G","download_json":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G.json","view_paper":"https://pith.science/paper/WKH6THR6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.11277&json=true","fetch_graph":"https://pith.science/api/pith-number/WKH6THR6MWXMFGH6AJMUIZU42G/graph.json","fetch_events":"https://pith.science/api/pith-number/WKH6THR6MWXMFGH6AJMUIZU42G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G/action/storage_attestation","attest_author":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G/action/author_attestation","sign_citation":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G/action/citation_signature","submit_replication":"https://pith.science/pith/WKH6THR6MWXMFGH6AJMUIZU42G/action/replication_record"}},"created_at":"2026-07-05T03:34:01.618425+00:00","updated_at":"2026-07-05T03:34:01.618425+00:00"}