{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TOYSPGZLJERX7GH4QDM5BQARBJ","short_pith_number":"pith:TOYSPGZL","schema_version":"1.0","canonical_sha256":"9bb1279b2b49237f98fc80d9d0c0110a7178ea367a7baa40a46e4ba63efd8613","source":{"kind":"arxiv","id":"2505.24106","version":1},"attestation_state":"computed","paper":{"title":"Controller Design for Bilinear Neural Feedback Loops","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Dhruv Shah, Jorge Cort\\'es","submitted_at":"2025-05-30T01:13:46Z","abstract_excerpt":"This paper considers a class of bilinear systems with a neural network in the loop. These arise naturally when employing machine learning techniques to approximate general, non-affine in the input, control systems. We propose a controller design framework that combines linear fractional representations and tools from linear parameter varying control to guarantee local exponential stability of a desired equilibrium. The controller is obtained from the solution of linear matrix inequalities, which can be solved offline, making the approach suitable for online applications. The proposed methodolo"},"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":"2505.24106","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-05-30T01:13:46Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"35d381df69ec73ca9f2437aa8e644b784940926285412d649dc6b23ad200fc96","abstract_canon_sha256":"b2f23c3389b368eecae4853835cf2e9504b714684e473f1199ec85a13a676042"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:41.975879Z","signature_b64":"oxTxBqZ3zWJ6y49zylhhrqfskJEPzMrVsf76owC0OGXGOIajuldzkzX/tixAqrr7MUF+DVURfx99KYgMIgrQAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bb1279b2b49237f98fc80d9d0c0110a7178ea367a7baa40a46e4ba63efd8613","last_reissued_at":"2026-07-05T11:12:41.975367Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:41.975367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Controller Design for Bilinear Neural Feedback Loops","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Dhruv Shah, Jorge Cort\\'es","submitted_at":"2025-05-30T01:13:46Z","abstract_excerpt":"This paper considers a class of bilinear systems with a neural network in the loop. These arise naturally when employing machine learning techniques to approximate general, non-affine in the input, control systems. We propose a controller design framework that combines linear fractional representations and tools from linear parameter varying control to guarantee local exponential stability of a desired equilibrium. The controller is obtained from the solution of linear matrix inequalities, which can be solved offline, making the approach suitable for online applications. The proposed methodolo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.24106","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/2505.24106/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":"2505.24106","created_at":"2026-07-05T11:12:41.975432+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.24106v1","created_at":"2026-07-05T11:12:41.975432+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.24106","created_at":"2026-07-05T11:12:41.975432+00:00"},{"alias_kind":"pith_short_12","alias_value":"TOYSPGZLJERX","created_at":"2026-07-05T11:12:41.975432+00:00"},{"alias_kind":"pith_short_16","alias_value":"TOYSPGZLJERX7GH4","created_at":"2026-07-05T11:12:41.975432+00:00"},{"alias_kind":"pith_short_8","alias_value":"TOYSPGZL","created_at":"2026-07-05T11:12:41.975432+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ","json":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ.json","graph_json":"https://pith.science/api/pith-number/TOYSPGZLJERX7GH4QDM5BQARBJ/graph.json","events_json":"https://pith.science/api/pith-number/TOYSPGZLJERX7GH4QDM5BQARBJ/events.json","paper":"https://pith.science/paper/TOYSPGZL"},"agent_actions":{"view_html":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ","download_json":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ.json","view_paper":"https://pith.science/paper/TOYSPGZL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.24106&json=true","fetch_graph":"https://pith.science/api/pith-number/TOYSPGZLJERX7GH4QDM5BQARBJ/graph.json","fetch_events":"https://pith.science/api/pith-number/TOYSPGZLJERX7GH4QDM5BQARBJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ/action/storage_attestation","attest_author":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ/action/author_attestation","sign_citation":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ/action/citation_signature","submit_replication":"https://pith.science/pith/TOYSPGZLJERX7GH4QDM5BQARBJ/action/replication_record"}},"created_at":"2026-07-05T11:12:41.975432+00:00","updated_at":"2026-07-05T11:12:41.975432+00:00"}