{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EWHQFTIO5JYGXEIVP4X66RAMBM","short_pith_number":"pith:EWHQFTIO","schema_version":"1.0","canonical_sha256":"258f02cd0eea706b91157f2fef440c0b25efbef0f4fb8616160bc862677aa444","source":{"kind":"arxiv","id":"2606.07529","version":1},"attestation_state":"computed","paper":{"title":"CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.MM"],"primary_cat":"cs.CL","authors_text":"Feng Zheng, Guanhua Chen, Shengli Zhou, Xiangchen Wang","submitted_at":"2026-04-21T02:43:28Z","abstract_excerpt":"Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task-relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we derive a key requirement for scene graph pruning: pre"},"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":"2606.07529","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-21T02:43:28Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.MM"],"title_canon_sha256":"7e3cc4ed9b0e9ff7ee940689543297fcfe655e425a014b18d63502645b87d0bd","abstract_canon_sha256":"4414804fd8036003f787883b70638f569e93130945f23952a55b8af9809c88c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T00:04:41.741543Z","signature_b64":"8tZZnYi4HYI19eZWw3HsTldr4sFlOGNf99Dv+03QiRmotmxKa423qhaq8qrx2E7thTY17LpGC7K9ydq9tBaLAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"258f02cd0eea706b91157f2fef440c0b25efbef0f4fb8616160bc862677aa444","last_reissued_at":"2026-06-09T00:04:41.740786Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T00:04:41.740786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.MM"],"primary_cat":"cs.CL","authors_text":"Feng Zheng, Guanhua Chen, Shengli Zhou, Xiangchen Wang","submitted_at":"2026-04-21T02:43:28Z","abstract_excerpt":"Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task-relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we derive a key requirement for scene graph pruning: pre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07529","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/2606.07529/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":"2606.07529","created_at":"2026-06-09T00:04:41.740896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.07529v1","created_at":"2026-06-09T00:04:41.740896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07529","created_at":"2026-06-09T00:04:41.740896+00:00"},{"alias_kind":"pith_short_12","alias_value":"EWHQFTIO5JYG","created_at":"2026-06-09T00:04:41.740896+00:00"},{"alias_kind":"pith_short_16","alias_value":"EWHQFTIO5JYGXEIV","created_at":"2026-06-09T00:04:41.740896+00:00"},{"alias_kind":"pith_short_8","alias_value":"EWHQFTIO","created_at":"2026-06-09T00:04:41.740896+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/EWHQFTIO5JYGXEIVP4X66RAMBM","json":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM.json","graph_json":"https://pith.science/api/pith-number/EWHQFTIO5JYGXEIVP4X66RAMBM/graph.json","events_json":"https://pith.science/api/pith-number/EWHQFTIO5JYGXEIVP4X66RAMBM/events.json","paper":"https://pith.science/paper/EWHQFTIO"},"agent_actions":{"view_html":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM","download_json":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM.json","view_paper":"https://pith.science/paper/EWHQFTIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.07529&json=true","fetch_graph":"https://pith.science/api/pith-number/EWHQFTIO5JYGXEIVP4X66RAMBM/graph.json","fetch_events":"https://pith.science/api/pith-number/EWHQFTIO5JYGXEIVP4X66RAMBM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM/action/storage_attestation","attest_author":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM/action/author_attestation","sign_citation":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM/action/citation_signature","submit_replication":"https://pith.science/pith/EWHQFTIO5JYGXEIVP4X66RAMBM/action/replication_record"}},"created_at":"2026-06-09T00:04:41.740896+00:00","updated_at":"2026-06-09T00:04:41.740896+00:00"}