{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ISPZPM4JIMJR3SQV3SJGGPLEBI","short_pith_number":"pith:ISPZPM4J","schema_version":"1.0","canonical_sha256":"449f97b38943131dca15dc92633d640a1099776d7cb18ab2d387ff71777654b6","source":{"kind":"arxiv","id":"2605.15975","version":1},"attestation_state":"computed","paper":{"title":"Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"Dillon Z. Chen, Sheila A. McIlraith, Till Hofmann, Toryn Q. Klassen","submitted_at":"2026-05-15T14:08:44Z","abstract_excerpt":"We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effective in training robots to solve a diversity of complex tasks requiring fine motor control and manipulation over low-level (LL), continuous environments. Yet, it remains a difficult endeavour to generate long-horizon plans from imitation learning alone. In contrast, high-level (HL), symbolic abstractions facilitate efficient and interpretable long-horizon planning. We propose to combine the strengths of LL imitation le"},"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":"2605.15975","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-15T14:08:44Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"2feb03183a5309f80268dcd17f36165fe9a0b40a921f211fc50c7f4bb35ea396","abstract_canon_sha256":"81bb7e485812f46e193b97a31396c7de5ef9cf32e3236267f100564dfb4cb13e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:47.310087Z","signature_b64":"eaon8nLwg6m0CKzDNsFi+IvrbYHNPduGJql2SmIdL/por8qQpUlyO2+6OoJkeGUt6oQUjbB6gHGJ2s7FOcD3Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"449f97b38943131dca15dc92633d640a1099776d7cb18ab2d387ff71777654b6","last_reissued_at":"2026-05-20T00:01:47.309503Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:47.309503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"Dillon Z. Chen, Sheila A. McIlraith, Till Hofmann, Toryn Q. Klassen","submitted_at":"2026-05-15T14:08:44Z","abstract_excerpt":"We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effective in training robots to solve a diversity of complex tasks requiring fine motor control and manipulation over low-level (LL), continuous environments. Yet, it remains a difficult endeavour to generate long-horizon plans from imitation learning alone. In contrast, high-level (HL), symbolic abstractions facilitate efficient and interpretable long-horizon planning. We propose to combine the strengths of LL imitation le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15975","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/2605.15975/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:44.865269Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.684939Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"450e247dbc78dd7930022b064e3cb5ce36a12ecea208c8e9339f719360d72c0b"},"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":"2605.15975","created_at":"2026-05-20T00:01:47.309585+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.15975v1","created_at":"2026-05-20T00:01:47.309585+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15975","created_at":"2026-05-20T00:01:47.309585+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISPZPM4JIMJR","created_at":"2026-05-20T00:01:47.309585+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISPZPM4JIMJR3SQV","created_at":"2026-05-20T00:01:47.309585+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISPZPM4J","created_at":"2026-05-20T00:01:47.309585+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/ISPZPM4JIMJR3SQV3SJGGPLEBI","json":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI.json","graph_json":"https://pith.science/api/pith-number/ISPZPM4JIMJR3SQV3SJGGPLEBI/graph.json","events_json":"https://pith.science/api/pith-number/ISPZPM4JIMJR3SQV3SJGGPLEBI/events.json","paper":"https://pith.science/paper/ISPZPM4J"},"agent_actions":{"view_html":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI","download_json":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI.json","view_paper":"https://pith.science/paper/ISPZPM4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.15975&json=true","fetch_graph":"https://pith.science/api/pith-number/ISPZPM4JIMJR3SQV3SJGGPLEBI/graph.json","fetch_events":"https://pith.science/api/pith-number/ISPZPM4JIMJR3SQV3SJGGPLEBI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI/action/storage_attestation","attest_author":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI/action/author_attestation","sign_citation":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI/action/citation_signature","submit_replication":"https://pith.science/pith/ISPZPM4JIMJR3SQV3SJGGPLEBI/action/replication_record"}},"created_at":"2026-05-20T00:01:47.309585+00:00","updated_at":"2026-05-20T00:01:47.309585+00:00"}