{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DFI3LUJOSZXGS7U7PXXOUAN5DZ","short_pith_number":"pith:DFI3LUJO","schema_version":"1.0","canonical_sha256":"1951b5d12e966e697e9f7deeea01bd1e6c51e035e7ff60729694ddbd26dd909d","source":{"kind":"arxiv","id":"2601.00126","version":3},"attestation_state":"computed","paper":{"title":"Compositional Diffusion with Guided Search for Long-Horizon Planning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Danfei Xu, David He, Utkarsh A Mishra, Yongxin Chen","submitted_at":"2025-12-31T22:03:19Z","abstract_excerpt":"Generative models have emerged as powerful tools for planning, with compositional approaches offering particular promise for modeling long-horizon task distributions by composing together local, modular generative models. This compositional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video generation. However, compositional generative models face a critical challenge: when local distributions are multimodal, existing composition methods average incompatible modes, producing plans that are neither locally feasible nor globally coher"},"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":"2601.00126","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.RO","submitted_at":"2025-12-31T22:03:19Z","cross_cats_sorted":[],"title_canon_sha256":"ee504b5038caeac46bd824ceda0895c09ae0e0f80dc8187c6e19481f87b96c17","abstract_canon_sha256":"82a26610ef96b9c0f3c97613412f0b117d79249c2fc80335daea6a136b5d02ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T00:19:54.327924Z","signature_b64":"1ajY0hyOgbg4N3TzDGtsYM9z8DITrvTYuUHlahC2+JHezF1CJf8mTkqoI6YXKAtQBk5jy9x1/asAEKTxPWvvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1951b5d12e966e697e9f7deeea01bd1e6c51e035e7ff60729694ddbd26dd909d","last_reissued_at":"2026-07-21T00:19:54.326938Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T00:19:54.326938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compositional Diffusion with Guided Search for Long-Horizon Planning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Danfei Xu, David He, Utkarsh A Mishra, Yongxin Chen","submitted_at":"2025-12-31T22:03:19Z","abstract_excerpt":"Generative models have emerged as powerful tools for planning, with compositional approaches offering particular promise for modeling long-horizon task distributions by composing together local, modular generative models. This compositional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video generation. However, compositional generative models face a critical challenge: when local distributions are multimodal, existing composition methods average incompatible modes, producing plans that are neither locally feasible nor globally coher"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.00126","kind":"arxiv","version":3},"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/2601.00126/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":"2601.00126","created_at":"2026-07-21T00:19:54.327414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.00126v3","created_at":"2026-07-21T00:19:54.327414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.00126","created_at":"2026-07-21T00:19:54.327414+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFI3LUJOSZXG","created_at":"2026-07-21T00:19:54.327414+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFI3LUJOSZXGS7U7","created_at":"2026-07-21T00:19:54.327414+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFI3LUJO","created_at":"2026-07-21T00:19:54.327414+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2606.21646","citing_title":"Energy-based Compositional Diffusion Planning","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16863","citing_title":"Plan First, Diffuse Later: Extrinsic Graph Guidance for Long-Horizon Diffusion Planning","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ","json":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ.json","graph_json":"https://pith.science/api/pith-number/DFI3LUJOSZXGS7U7PXXOUAN5DZ/graph.json","events_json":"https://pith.science/api/pith-number/DFI3LUJOSZXGS7U7PXXOUAN5DZ/events.json","paper":"https://pith.science/paper/DFI3LUJO"},"agent_actions":{"view_html":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ","download_json":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ.json","view_paper":"https://pith.science/paper/DFI3LUJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.00126&json=true","fetch_graph":"https://pith.science/api/pith-number/DFI3LUJOSZXGS7U7PXXOUAN5DZ/graph.json","fetch_events":"https://pith.science/api/pith-number/DFI3LUJOSZXGS7U7PXXOUAN5DZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ/action/storage_attestation","attest_author":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ/action/author_attestation","sign_citation":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ/action/citation_signature","submit_replication":"https://pith.science/pith/DFI3LUJOSZXGS7U7PXXOUAN5DZ/action/replication_record"}},"created_at":"2026-07-21T00:19:54.327414+00:00","updated_at":"2026-07-21T00:19:54.327414+00:00"}