{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IY4D5NOQJGIDL7RDWTUP5RE4WW","short_pith_number":"pith:IY4D5NOQ","schema_version":"1.0","canonical_sha256":"46383eb5d0499035fe23b4e8fec49cb5865c3ada66e7159992a63eb060deae83","source":{"kind":"arxiv","id":"2512.18540","version":2},"attestation_state":"computed","paper":{"title":"Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"John Cao, Luca Furieri","submitted_at":"2025-12-20T23:35:07Z","abstract_excerpt":"We study distributed control of networked systems through reinforcement learning, where neural policies must be simultaneously scalable, expressive and stabilizing. We introduce a policy parameterization that embeds Graph Neural Networks (GNNs) into a Youla-like magnitude-direction parameterization, yielding distributed stochastic controllers that guarantee network-level closed-loop stability by design. The magnitude is implemented as a stable operator consisting of a GNN acting on disturbance feedback, while the direction is a GNN acting on local observations. We prove robustness of the polic"},"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":"2512.18540","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-12-20T23:35:07Z","cross_cats_sorted":["cs.LG","cs.SY","math.OC"],"title_canon_sha256":"40c48ecbfdafcc810dceb963c88952bd659c0f7de89309428e7eb2d539e20384","abstract_canon_sha256":"ad82a55d475cd414bd1a0b54109dcca825f0753b5febd4cf491950fa8bd473f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T02:05:09.610796Z","signature_b64":"o00EPF7c2VMgg7a0QGQ/unyW+6XlX0ScgDx+ZBwWduwbK1rdtTxhRVjBWJrfD5v6xmyAwblgtM+qJwnwWiTOCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46383eb5d0499035fe23b4e8fec49cb5865c3ada66e7159992a63eb060deae83","last_reissued_at":"2026-05-27T02:05:09.610086Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T02:05:09.610086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"John Cao, Luca Furieri","submitted_at":"2025-12-20T23:35:07Z","abstract_excerpt":"We study distributed control of networked systems through reinforcement learning, where neural policies must be simultaneously scalable, expressive and stabilizing. We introduce a policy parameterization that embeds Graph Neural Networks (GNNs) into a Youla-like magnitude-direction parameterization, yielding distributed stochastic controllers that guarantee network-level closed-loop stability by design. The magnitude is implemented as a stable operator consisting of a GNN acting on disturbance feedback, while the direction is a GNN acting on local observations. We prove robustness of the polic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.18540","kind":"arxiv","version":2},"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/2512.18540/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":"2512.18540","created_at":"2026-05-27T02:05:09.610187+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.18540v2","created_at":"2026-05-27T02:05:09.610187+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.18540","created_at":"2026-05-27T02:05:09.610187+00:00"},{"alias_kind":"pith_short_12","alias_value":"IY4D5NOQJGID","created_at":"2026-05-27T02:05:09.610187+00:00"},{"alias_kind":"pith_short_16","alias_value":"IY4D5NOQJGIDL7RD","created_at":"2026-05-27T02:05:09.610187+00:00"},{"alias_kind":"pith_short_8","alias_value":"IY4D5NOQ","created_at":"2026-05-27T02:05:09.610187+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/IY4D5NOQJGIDL7RDWTUP5RE4WW","json":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW.json","graph_json":"https://pith.science/api/pith-number/IY4D5NOQJGIDL7RDWTUP5RE4WW/graph.json","events_json":"https://pith.science/api/pith-number/IY4D5NOQJGIDL7RDWTUP5RE4WW/events.json","paper":"https://pith.science/paper/IY4D5NOQ"},"agent_actions":{"view_html":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW","download_json":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW.json","view_paper":"https://pith.science/paper/IY4D5NOQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.18540&json=true","fetch_graph":"https://pith.science/api/pith-number/IY4D5NOQJGIDL7RDWTUP5RE4WW/graph.json","fetch_events":"https://pith.science/api/pith-number/IY4D5NOQJGIDL7RDWTUP5RE4WW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW/action/storage_attestation","attest_author":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW/action/author_attestation","sign_citation":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW/action/citation_signature","submit_replication":"https://pith.science/pith/IY4D5NOQJGIDL7RDWTUP5RE4WW/action/replication_record"}},"created_at":"2026-05-27T02:05:09.610187+00:00","updated_at":"2026-05-27T02:05:09.610187+00:00"}