{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V2ISHYQFI2KXFG6C5HK6CJRBE5","short_pith_number":"pith:V2ISHYQF","schema_version":"1.0","canonical_sha256":"ae9123e2054695729bc2e9d5e126212764cb49fb5fb8cf40da462490e40293aa","source":{"kind":"arxiv","id":"2505.07054","version":1},"attestation_state":"computed","paper":{"title":"YANNs: Y-wise Affine Neural Networks for Exact and Efficient Representations of Piecewise Linear Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Austin Braniff, Yuhe Tian","submitted_at":"2025-05-11T16:55:38Z","abstract_excerpt":"This work formally introduces Y-wise Affine Neural Networks (YANNs), a fully-explainable network architecture that continuously and efficiently represent piecewise affine functions with polytopic subdomains. Following from the proofs, it is shown that the development of YANNs requires no training to achieve the functionally equivalent representation. YANNs thus maintain all mathematical properties of the original formulations. Multi-parametric model predictive control is utilized as an application showcase of YANNs, which theoretically computes optimal control laws as a piecewise affine functi"},"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":"2505.07054","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-05-11T16:55:38Z","cross_cats_sorted":["cs.LG","cs.SY","math.OC"],"title_canon_sha256":"0a447a4b6cdd17c10f30b83dc3534283717a30211a28bd2e716e6397536e3350","abstract_canon_sha256":"2309e35c49b754db700007fa36953f5d96cfe4ece7e11ce4e9ceeb2541a92427"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:05.927299Z","signature_b64":"6oi2Om4KXsgosvnjm2qc0PJKK2uOzydadEeyDL704qlyp2LFn0/G2HLFD9OS6Wc/uzlYUi48GN2UjgZtTzk+AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae9123e2054695729bc2e9d5e126212764cb49fb5fb8cf40da462490e40293aa","last_reissued_at":"2026-05-21T01:05:05.926427Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:05.926427Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"YANNs: Y-wise Affine Neural Networks for Exact and Efficient Representations of Piecewise Linear Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Austin Braniff, Yuhe Tian","submitted_at":"2025-05-11T16:55:38Z","abstract_excerpt":"This work formally introduces Y-wise Affine Neural Networks (YANNs), a fully-explainable network architecture that continuously and efficiently represent piecewise affine functions with polytopic subdomains. Following from the proofs, it is shown that the development of YANNs requires no training to achieve the functionally equivalent representation. YANNs thus maintain all mathematical properties of the original formulations. Multi-parametric model predictive control is utilized as an application showcase of YANNs, which theoretically computes optimal control laws as a piecewise affine functi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.07054","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/2505.07054/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":"2505.07054","created_at":"2026-05-21T01:05:05.926564+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.07054v1","created_at":"2026-05-21T01:05:05.926564+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07054","created_at":"2026-05-21T01:05:05.926564+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2ISHYQFI2KX","created_at":"2026-05-21T01:05:05.926564+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2ISHYQFI2KXFG6C","created_at":"2026-05-21T01:05:05.926564+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2ISHYQF","created_at":"2026-05-21T01:05:05.926564+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2508.16474","citing_title":"Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)","ref_index":49,"is_internal_anchor":true},{"citing_arxiv_id":"2605.21211","citing_title":"Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5","json":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5.json","graph_json":"https://pith.science/api/pith-number/V2ISHYQFI2KXFG6C5HK6CJRBE5/graph.json","events_json":"https://pith.science/api/pith-number/V2ISHYQFI2KXFG6C5HK6CJRBE5/events.json","paper":"https://pith.science/paper/V2ISHYQF"},"agent_actions":{"view_html":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5","download_json":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5.json","view_paper":"https://pith.science/paper/V2ISHYQF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.07054&json=true","fetch_graph":"https://pith.science/api/pith-number/V2ISHYQFI2KXFG6C5HK6CJRBE5/graph.json","fetch_events":"https://pith.science/api/pith-number/V2ISHYQFI2KXFG6C5HK6CJRBE5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5/action/storage_attestation","attest_author":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5/action/author_attestation","sign_citation":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5/action/citation_signature","submit_replication":"https://pith.science/pith/V2ISHYQFI2KXFG6C5HK6CJRBE5/action/replication_record"}},"created_at":"2026-05-21T01:05:05.926564+00:00","updated_at":"2026-05-21T01:05:05.926564+00:00"}