{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AEX5AB3AQXGM6H5Y3VG45LNW57","short_pith_number":"pith:AEX5AB3A","schema_version":"1.0","canonical_sha256":"012fd0076085cccf1fb8dd4dceadb6efc771596fcab1e9d335e38fde52f67b5e","source":{"kind":"arxiv","id":"2601.03509","version":2},"attestation_state":"computed","paper":{"title":"Evolving Programmatic Skill Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AI","authors_text":"Bang Liu, Haochen Shi, Xingdi Yuan","submitted_at":"2026-01-07T01:43:25Z","abstract_excerpt":"We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)~\\opreflect for structured fault localization over skill compositions, (2)~progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaini"},"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":"2601.03509","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-07T01:43:25Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"e9d6f94e7123080deab89dc6bd0269a812f3e6a426dc9f5adab57f02ad0e25dd","abstract_canon_sha256":"3785e83a95d77dc3767664aa57c5e9dcc8a2f8b18bf1ff8817003c65e3c4c2e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T00:14:22.288857Z","signature_b64":"iCwG/nET6JhopX5+wgs6kZqc2cY9CusEX+sfDCfwG+6XjIINWExl0tTfofUj1uR+ZJIasQ53LFETDLphpS9VAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"012fd0076085cccf1fb8dd4dceadb6efc771596fcab1e9d335e38fde52f67b5e","last_reissued_at":"2026-06-24T00:14:22.288417Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T00:14:22.288417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evolving Programmatic Skill Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AI","authors_text":"Bang Liu, Haochen Shi, Xingdi Yuan","submitted_at":"2026-01-07T01:43:25Z","abstract_excerpt":"We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)~\\opreflect for structured fault localization over skill compositions, (2)~progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.03509","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/2601.03509/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":"2601.03509","created_at":"2026-06-24T00:14:22.288473+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.03509v2","created_at":"2026-06-24T00:14:22.288473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.03509","created_at":"2026-06-24T00:14:22.288473+00:00"},{"alias_kind":"pith_short_12","alias_value":"AEX5AB3AQXGM","created_at":"2026-06-24T00:14:22.288473+00:00"},{"alias_kind":"pith_short_16","alias_value":"AEX5AB3AQXGM6H5Y","created_at":"2026-06-24T00:14:22.288473+00:00"},{"alias_kind":"pith_short_8","alias_value":"AEX5AB3A","created_at":"2026-06-24T00:14:22.288473+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":6,"sample":[{"citing_arxiv_id":"2606.08091","citing_title":"VideoWeaver: Evaluating and Evolving Skills for Agentic Long Video Generation","ref_index":42,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07358","citing_title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","ref_index":54,"is_internal_anchor":true},{"citing_arxiv_id":"2605.21463","citing_title":"Mem-$\\pi$: Adaptive Memory through Learning When and What to Generate","ref_index":38,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07358","citing_title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","ref_index":56,"is_internal_anchor":true},{"citing_arxiv_id":"2605.14477","citing_title":"Test-Time Learning with an Evolving Library","ref_index":27,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07358","citing_title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","ref_index":56,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57","json":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57.json","graph_json":"https://pith.science/api/pith-number/AEX5AB3AQXGM6H5Y3VG45LNW57/graph.json","events_json":"https://pith.science/api/pith-number/AEX5AB3AQXGM6H5Y3VG45LNW57/events.json","paper":"https://pith.science/paper/AEX5AB3A"},"agent_actions":{"view_html":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57","download_json":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57.json","view_paper":"https://pith.science/paper/AEX5AB3A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.03509&json=true","fetch_graph":"https://pith.science/api/pith-number/AEX5AB3AQXGM6H5Y3VG45LNW57/graph.json","fetch_events":"https://pith.science/api/pith-number/AEX5AB3AQXGM6H5Y3VG45LNW57/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57/action/storage_attestation","attest_author":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57/action/author_attestation","sign_citation":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57/action/citation_signature","submit_replication":"https://pith.science/pith/AEX5AB3AQXGM6H5Y3VG45LNW57/action/replication_record"}},"created_at":"2026-06-24T00:14:22.288473+00:00","updated_at":"2026-06-24T00:14:22.288473+00:00"}