{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:K3YHYPHF43K7EP4S6Y6BCW67OJ","short_pith_number":"pith:K3YHYPHF","schema_version":"1.0","canonical_sha256":"56f07c3ce5e6d5f23f92f63c115bdf725426ed1cafb02444477d85388a1b8dd1","source":{"kind":"arxiv","id":"2608.00908","version":1},"attestation_state":"computed","paper":{"title":"Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Tian Lan, Vaneet Aggarwal, Zuyuan Zhang","submitted_at":"2026-08-02T00:27:01Z","abstract_excerpt":"Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to optimize, while constraints are commonly handled through penalty, barrier, or Lagrangian mechanisms. We observe that before a value function can certify the best deployment, intermediate signals may already identify many candidates that should be excluded from further optimization. This motivates a complementary direction: \\emph{Learning Not to Optimize}. Before a value function is accurate enough to select the best placement-control decision, in"},"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":"2608.00908","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2026-08-02T00:27:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d6246cbb32e97202e680412c50a73cfc3a28fccb231dc4c5bddbf60a8b203751","abstract_canon_sha256":"150525ed076c6200ff5c9fa53952f821fed2f23c2861bdba8e1feb70e3e7ec3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T01:55:31.570029Z","signature_b64":"B5cFM3pyPblHXN5YLevDB06cezLecqFypj37npzcdoa6qtMJ6FoYJOi3um/yA4KIDELFcRk6e/T8cC8gnDfZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56f07c3ce5e6d5f23f92f63c115bdf725426ed1cafb02444477d85388a1b8dd1","last_reissued_at":"2026-08-04T01:55:31.568394Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T01:55:31.568394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Tian Lan, Vaneet Aggarwal, Zuyuan Zhang","submitted_at":"2026-08-02T00:27:01Z","abstract_excerpt":"Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to optimize, while constraints are commonly handled through penalty, barrier, or Lagrangian mechanisms. We observe that before a value function can certify the best deployment, intermediate signals may already identify many candidates that should be excluded from further optimization. This motivates a complementary direction: \\emph{Learning Not to Optimize}. Before a value function is accurate enough to select the best placement-control decision, in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00908","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/2608.00908/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":"2608.00908","created_at":"2026-08-04T01:55:31.569751+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.00908v1","created_at":"2026-08-04T01:55:31.569751+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.00908","created_at":"2026-08-04T01:55:31.569751+00:00"},{"alias_kind":"pith_short_12","alias_value":"K3YHYPHF43K7","created_at":"2026-08-04T01:55:31.569751+00:00"},{"alias_kind":"pith_short_16","alias_value":"K3YHYPHF43K7EP4S","created_at":"2026-08-04T01:55:31.569751+00:00"},{"alias_kind":"pith_short_8","alias_value":"K3YHYPHF","created_at":"2026-08-04T01:55:31.569751+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/K3YHYPHF43K7EP4S6Y6BCW67OJ","json":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ.json","graph_json":"https://pith.science/api/pith-number/K3YHYPHF43K7EP4S6Y6BCW67OJ/graph.json","events_json":"https://pith.science/api/pith-number/K3YHYPHF43K7EP4S6Y6BCW67OJ/events.json","paper":"https://pith.science/paper/K3YHYPHF"},"agent_actions":{"view_html":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ","download_json":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ.json","view_paper":"https://pith.science/paper/K3YHYPHF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.00908&json=true","fetch_graph":"https://pith.science/api/pith-number/K3YHYPHF43K7EP4S6Y6BCW67OJ/graph.json","fetch_events":"https://pith.science/api/pith-number/K3YHYPHF43K7EP4S6Y6BCW67OJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ/action/storage_attestation","attest_author":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ/action/author_attestation","sign_citation":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ/action/citation_signature","submit_replication":"https://pith.science/pith/K3YHYPHF43K7EP4S6Y6BCW67OJ/action/replication_record"}},"created_at":"2026-08-04T01:55:31.569751+00:00","updated_at":"2026-08-04T01:55:31.569751+00:00"}