{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QG3W4U6QKPLVJGNFNZYOM2CN3L","short_pith_number":"pith:QG3W4U6Q","schema_version":"1.0","canonical_sha256":"81b76e53d053d75499a56e70e6684ddad98c8ddd740b5e94011715b01edbbc76","source":{"kind":"arxiv","id":"2606.03512","version":1},"attestation_state":"computed","paper":{"title":"SPADE: Sketch-guided Path Planning Augmented with Diffusion Experts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Anthony Rizk, Charbel Abi Hana, Mikael Khalil, Tatiana Ghantous","submitted_at":"2026-06-02T11:29:00Z","abstract_excerpt":"Path planning is essential for Autonomous Mobile Robots (AMRs). Conventional methods for incorporating human preferences into planning typically rely on either complex reward engineering or hardware-intensive solutions. Recent state-of-the-art frameworks leverage imitation learning to train behavior-specific path planning models from expert demonstrations. However, these approaches face two key limitations: limited generalization to unseen environments and low robustness in demonstration collection. To address these challenges, this work introduces an enhanced framework that focuses on two mai"},"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":"2606.03512","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-02T11:29:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9cce3ec4cad689df063940e822e17967334aaa2b8ec51a3d9446d77c6db567f9","abstract_canon_sha256":"a99cbd399f762a066a3a6ca04626cc20e42da0354fbd85ae6e7bc82b2317e2db"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:59.699568Z","signature_b64":"63C7gtNHs71p8yEFUpT6tGpvpiSk3gNQKSJCs9dLy3NbKAIq5WvNaO8qHtuG4VAvxM/9SnaZvWNlasFmhu9mAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81b76e53d053d75499a56e70e6684ddad98c8ddd740b5e94011715b01edbbc76","last_reissued_at":"2026-06-03T01:05:59.699195Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:59.699195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SPADE: Sketch-guided Path Planning Augmented with Diffusion Experts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Anthony Rizk, Charbel Abi Hana, Mikael Khalil, Tatiana Ghantous","submitted_at":"2026-06-02T11:29:00Z","abstract_excerpt":"Path planning is essential for Autonomous Mobile Robots (AMRs). Conventional methods for incorporating human preferences into planning typically rely on either complex reward engineering or hardware-intensive solutions. Recent state-of-the-art frameworks leverage imitation learning to train behavior-specific path planning models from expert demonstrations. However, these approaches face two key limitations: limited generalization to unseen environments and low robustness in demonstration collection. To address these challenges, this work introduces an enhanced framework that focuses on two mai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03512","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/2606.03512/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":"2606.03512","created_at":"2026-06-03T01:05:59.699257+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.03512v1","created_at":"2026-06-03T01:05:59.699257+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03512","created_at":"2026-06-03T01:05:59.699257+00:00"},{"alias_kind":"pith_short_12","alias_value":"QG3W4U6QKPLV","created_at":"2026-06-03T01:05:59.699257+00:00"},{"alias_kind":"pith_short_16","alias_value":"QG3W4U6QKPLVJGNF","created_at":"2026-06-03T01:05:59.699257+00:00"},{"alias_kind":"pith_short_8","alias_value":"QG3W4U6Q","created_at":"2026-06-03T01:05:59.699257+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/QG3W4U6QKPLVJGNFNZYOM2CN3L","json":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L.json","graph_json":"https://pith.science/api/pith-number/QG3W4U6QKPLVJGNFNZYOM2CN3L/graph.json","events_json":"https://pith.science/api/pith-number/QG3W4U6QKPLVJGNFNZYOM2CN3L/events.json","paper":"https://pith.science/paper/QG3W4U6Q"},"agent_actions":{"view_html":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L","download_json":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L.json","view_paper":"https://pith.science/paper/QG3W4U6Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.03512&json=true","fetch_graph":"https://pith.science/api/pith-number/QG3W4U6QKPLVJGNFNZYOM2CN3L/graph.json","fetch_events":"https://pith.science/api/pith-number/QG3W4U6QKPLVJGNFNZYOM2CN3L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L/action/storage_attestation","attest_author":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L/action/author_attestation","sign_citation":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L/action/citation_signature","submit_replication":"https://pith.science/pith/QG3W4U6QKPLVJGNFNZYOM2CN3L/action/replication_record"}},"created_at":"2026-06-03T01:05:59.699257+00:00","updated_at":"2026-06-03T01:05:59.699257+00:00"}