{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KZ47MROZVUU6BCTXCK6FOOO4UP","short_pith_number":"pith:KZ47MROZ","schema_version":"1.0","canonical_sha256":"5679f645d9ad29e08a7712bc5739dca3d08b98e2fc505591c441dbca8d72ee96","source":{"kind":"arxiv","id":"2605.24570","version":1},"attestation_state":"computed","paper":{"title":"PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Lama Ayash, Muhammad Mubashar, Naeemullah Khan, Sattam Altuuaim","submitted_at":"2026-05-23T13:10:15Z","abstract_excerpt":"Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training begins. This static design can limit their ability to respond to changing gradient behavior across the loss landscape, where training may shift between stable, noisy, and inconsistent regimes.\n  This study proposes PILOT (Policy-Informed Learned OpTimizer), an online optimizer that adapts its update behavior during training. Rather than using a fixed balance between momentum, normalization, and sign-based updates, PILOT uses gradient-direction agre"},"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":"2605.24570","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-23T13:10:15Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"a18634488a360c6ba39ef0a9b52e45ba77c8e5be3a0b2731ad56e652c9bc140a","abstract_canon_sha256":"3f2989232ff4a4e2f0b7ff229a5c80ae2d1a08bc7e98e82cbacea83fba394172"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:46.875070Z","signature_b64":"dhUIlNosiw9MzjY1sYYCsR+TPG+0KCLTV0L26/7PnzLQk+fwpKfx8/ctW632aOEjVc0T2ZEBmvxjf6PK3mBmDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5679f645d9ad29e08a7712bc5739dca3d08b98e2fc505591c441dbca8d72ee96","last_reissued_at":"2026-05-26T01:03:46.874092Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:46.874092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Lama Ayash, Muhammad Mubashar, Naeemullah Khan, Sattam Altuuaim","submitted_at":"2026-05-23T13:10:15Z","abstract_excerpt":"Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training begins. This static design can limit their ability to respond to changing gradient behavior across the loss landscape, where training may shift between stable, noisy, and inconsistent regimes.\n  This study proposes PILOT (Policy-Informed Learned OpTimizer), an online optimizer that adapts its update behavior during training. Rather than using a fixed balance between momentum, normalization, and sign-based updates, PILOT uses gradient-direction agre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24570","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/2605.24570/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":"2605.24570","created_at":"2026-05-26T01:03:46.874261+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.24570v1","created_at":"2026-05-26T01:03:46.874261+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24570","created_at":"2026-05-26T01:03:46.874261+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZ47MROZVUU6","created_at":"2026-05-26T01:03:46.874261+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZ47MROZVUU6BCTX","created_at":"2026-05-26T01:03:46.874261+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZ47MROZ","created_at":"2026-05-26T01:03:46.874261+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/KZ47MROZVUU6BCTXCK6FOOO4UP","json":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP.json","graph_json":"https://pith.science/api/pith-number/KZ47MROZVUU6BCTXCK6FOOO4UP/graph.json","events_json":"https://pith.science/api/pith-number/KZ47MROZVUU6BCTXCK6FOOO4UP/events.json","paper":"https://pith.science/paper/KZ47MROZ"},"agent_actions":{"view_html":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP","download_json":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP.json","view_paper":"https://pith.science/paper/KZ47MROZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.24570&json=true","fetch_graph":"https://pith.science/api/pith-number/KZ47MROZVUU6BCTXCK6FOOO4UP/graph.json","fetch_events":"https://pith.science/api/pith-number/KZ47MROZVUU6BCTXCK6FOOO4UP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP/action/storage_attestation","attest_author":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP/action/author_attestation","sign_citation":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP/action/citation_signature","submit_replication":"https://pith.science/pith/KZ47MROZVUU6BCTXCK6FOOO4UP/action/replication_record"}},"created_at":"2026-05-26T01:03:46.874261+00:00","updated_at":"2026-05-26T01:03:46.874261+00:00"}