{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NG5YD5VSHISI6QFXKRDXVUCDZU","short_pith_number":"pith:NG5YD5VS","schema_version":"1.0","canonical_sha256":"69bb81f6b23a248f40b754477ad043cd12874bd4c1f8f5766fd30d30a979cf65","source":{"kind":"arxiv","id":"2412.11396","version":1},"attestation_state":"computed","paper":{"title":"Leveraging Retrieval-Augmented Tags for Large Vision-Language Understanding in Complex Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anthony Moore, Antonio Carlos Rivera, Steven Robinson","submitted_at":"2024-12-16T02:52:19Z","abstract_excerpt":"Object-aware reasoning in vision-language tasks poses significant challenges for current models, particularly in handling unseen objects, reducing hallucinations, and capturing fine-grained relationships in complex visual scenes. To address these limitations, we propose the Vision-Aware Retrieval-Augmented Prompting (VRAP) framework, a generative approach that enhances Large Vision-Language Models (LVLMs) by integrating retrieval-augmented object tags into their prompts. VRAP introduces a novel pipeline where structured tags, including objects, attributes, and relationships, are extracted usin"},"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":"2412.11396","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-16T02:52:19Z","cross_cats_sorted":[],"title_canon_sha256":"aa49e73b08a8b660951037a46e072fc798457ea87429dd503cbd7b7990cce109","abstract_canon_sha256":"896fa9693625872005e073318398e12b78ce75e36cc9a093d588c08ce83c0c30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:32.132245Z","signature_b64":"bwuqSUtQJaRAqHCLmSY6NdXdB2FIXgDVc/mp+W9xbpevR5GZ8i4rEMTdq+92NJ2PWjZluJbVLxpMaUOr5zTpDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69bb81f6b23a248f40b754477ad043cd12874bd4c1f8f5766fd30d30a979cf65","last_reissued_at":"2026-07-05T09:49:32.131790Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:32.131790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging Retrieval-Augmented Tags for Large Vision-Language Understanding in Complex Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anthony Moore, Antonio Carlos Rivera, Steven Robinson","submitted_at":"2024-12-16T02:52:19Z","abstract_excerpt":"Object-aware reasoning in vision-language tasks poses significant challenges for current models, particularly in handling unseen objects, reducing hallucinations, and capturing fine-grained relationships in complex visual scenes. To address these limitations, we propose the Vision-Aware Retrieval-Augmented Prompting (VRAP) framework, a generative approach that enhances Large Vision-Language Models (LVLMs) by integrating retrieval-augmented object tags into their prompts. VRAP introduces a novel pipeline where structured tags, including objects, attributes, and relationships, are extracted usin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11396","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/2412.11396/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":"2412.11396","created_at":"2026-07-05T09:49:32.131842+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.11396v1","created_at":"2026-07-05T09:49:32.131842+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11396","created_at":"2026-07-05T09:49:32.131842+00:00"},{"alias_kind":"pith_short_12","alias_value":"NG5YD5VSHISI","created_at":"2026-07-05T09:49:32.131842+00:00"},{"alias_kind":"pith_short_16","alias_value":"NG5YD5VSHISI6QFX","created_at":"2026-07-05T09:49:32.131842+00:00"},{"alias_kind":"pith_short_8","alias_value":"NG5YD5VS","created_at":"2026-07-05T09:49:32.131842+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/NG5YD5VSHISI6QFXKRDXVUCDZU","json":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU.json","graph_json":"https://pith.science/api/pith-number/NG5YD5VSHISI6QFXKRDXVUCDZU/graph.json","events_json":"https://pith.science/api/pith-number/NG5YD5VSHISI6QFXKRDXVUCDZU/events.json","paper":"https://pith.science/paper/NG5YD5VS"},"agent_actions":{"view_html":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU","download_json":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU.json","view_paper":"https://pith.science/paper/NG5YD5VS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.11396&json=true","fetch_graph":"https://pith.science/api/pith-number/NG5YD5VSHISI6QFXKRDXVUCDZU/graph.json","fetch_events":"https://pith.science/api/pith-number/NG5YD5VSHISI6QFXKRDXVUCDZU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU/action/storage_attestation","attest_author":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU/action/author_attestation","sign_citation":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU/action/citation_signature","submit_replication":"https://pith.science/pith/NG5YD5VSHISI6QFXKRDXVUCDZU/action/replication_record"}},"created_at":"2026-07-05T09:49:32.131842+00:00","updated_at":"2026-07-05T09:49:32.131842+00:00"}