{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VD2QJI65RSN2UPD3G4ADY32XR3","short_pith_number":"pith:VD2QJI65","schema_version":"1.0","canonical_sha256":"a8f504a3dd8c9baa3c7b37003c6f578ee7c89e58393812a7a3411cd16bd824df","source":{"kind":"arxiv","id":"2606.22694","version":1},"attestation_state":"computed","paper":{"title":"SATURN: Symbolic Spatial Reasoning for Multi-Perspective Grounding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SC"],"primary_cat":"cs.CV","authors_text":"Amir Zadeh, Chuan Li, Danial Kamali, Parisa Kordjamshidi, Shreya Rajpal, Tanawan Premsri","submitted_at":"2026-06-21T22:15:48Z","abstract_excerpt":"Vision-Language Models (VLMs) remain unreliable when spatial reasoning requires composing relations whose meanings depend on frames of reference. Existing neuro-symbolic methods make reasoning more explicit, but often depend on brittle geometric procedures and hard decisions over noisy perception. We propose SATURN, a neuro-symbolic framework for perspective-aware compositional spatial reasoning. SATURN reconstructs an approximate 3D scene, derives soft perspective-aware spatial predicates, and composes them with a training-free Pythonic symbolic executor, separating perception from reasoning "},"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":"2606.22694","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-21T22:15:48Z","cross_cats_sorted":["cs.SC"],"title_canon_sha256":"c223c9ab100231d6df156fb4698579bb7a29396bfa6a9243aa070de00dd7091d","abstract_canon_sha256":"b36168976f29c285937ee3b56bda0ca8bc5e9754453e2e046dbb03d44f86129a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:45.049329Z","signature_b64":"2VxNtsmlGwOrB32S/3JLDKoolIESgFEIneQ7LEokOjPvAyfEM0KqpTvhVqYTttEoyiAgQG6T+6D1IxT3Lp9TCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8f504a3dd8c9baa3c7b37003c6f578ee7c89e58393812a7a3411cd16bd824df","last_reissued_at":"2026-06-23T02:13:45.048953Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:45.048953Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SATURN: Symbolic Spatial Reasoning for Multi-Perspective Grounding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SC"],"primary_cat":"cs.CV","authors_text":"Amir Zadeh, Chuan Li, Danial Kamali, Parisa Kordjamshidi, Shreya Rajpal, Tanawan Premsri","submitted_at":"2026-06-21T22:15:48Z","abstract_excerpt":"Vision-Language Models (VLMs) remain unreliable when spatial reasoning requires composing relations whose meanings depend on frames of reference. Existing neuro-symbolic methods make reasoning more explicit, but often depend on brittle geometric procedures and hard decisions over noisy perception. We propose SATURN, a neuro-symbolic framework for perspective-aware compositional spatial reasoning. SATURN reconstructs an approximate 3D scene, derives soft perspective-aware spatial predicates, and composes them with a training-free Pythonic symbolic executor, separating perception from reasoning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.22694","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/2606.22694/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":"2606.22694","created_at":"2026-06-23T02:13:45.049022+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.22694v1","created_at":"2026-06-23T02:13:45.049022+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.22694","created_at":"2026-06-23T02:13:45.049022+00:00"},{"alias_kind":"pith_short_12","alias_value":"VD2QJI65RSN2","created_at":"2026-06-23T02:13:45.049022+00:00"},{"alias_kind":"pith_short_16","alias_value":"VD2QJI65RSN2UPD3","created_at":"2026-06-23T02:13:45.049022+00:00"},{"alias_kind":"pith_short_8","alias_value":"VD2QJI65","created_at":"2026-06-23T02:13:45.049022+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/VD2QJI65RSN2UPD3G4ADY32XR3","json":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3.json","graph_json":"https://pith.science/api/pith-number/VD2QJI65RSN2UPD3G4ADY32XR3/graph.json","events_json":"https://pith.science/api/pith-number/VD2QJI65RSN2UPD3G4ADY32XR3/events.json","paper":"https://pith.science/paper/VD2QJI65"},"agent_actions":{"view_html":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3","download_json":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3.json","view_paper":"https://pith.science/paper/VD2QJI65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.22694&json=true","fetch_graph":"https://pith.science/api/pith-number/VD2QJI65RSN2UPD3G4ADY32XR3/graph.json","fetch_events":"https://pith.science/api/pith-number/VD2QJI65RSN2UPD3G4ADY32XR3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3/action/storage_attestation","attest_author":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3/action/author_attestation","sign_citation":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3/action/citation_signature","submit_replication":"https://pith.science/pith/VD2QJI65RSN2UPD3G4ADY32XR3/action/replication_record"}},"created_at":"2026-06-23T02:13:45.049022+00:00","updated_at":"2026-06-23T02:13:45.049022+00:00"}