{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B6L76PTHRTMHIRLF23LF3NANXI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f55b84f8a9e8ed39df0527767569ad7849c1fb159b034b780d8c313c9bb45120","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-02-18T22:22:44Z","title_canon_sha256":"8d0572b7cd8f53e75751bee0d854b773a61c2a950897c7fa5d7b136e4ea56d90"},"schema_version":"1.0","source":{"id":"2602.16918","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.16918","created_at":"2026-07-15T00:21:16Z"},{"alias_kind":"arxiv_version","alias_value":"2602.16918v2","created_at":"2026-07-15T00:21:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.16918","created_at":"2026-07-15T00:21:16Z"},{"alias_kind":"pith_short_12","alias_value":"B6L76PTHRTMH","created_at":"2026-07-15T00:21:16Z"},{"alias_kind":"pith_short_16","alias_value":"B6L76PTHRTMHIRLF","created_at":"2026-07-15T00:21:16Z"},{"alias_kind":"pith_short_8","alias_value":"B6L76PTH","created_at":"2026-07-15T00:21:16Z"}],"graph_snapshots":[{"event_id":"sha256:5c69ee16df2e11e3e13b76dee10d189af59c9e99e0c7615f2b4a05adc50e1564","target":"graph","created_at":"2026-07-15T00:21:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2602.16918/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Xray-Visual, a unified vision model architecture for large-scale image and video understanding trained on industry-scale social media data. Our model leverages over 15 billion curated image-text pairs and 10 billion video-hashtag pairs from Facebook and Instagram, employing robust data curation pipelines that incorporate balancing and noise suppression strategies to maximize semantic diversity while minimizing label noise. We introduce a three-stage training pipeline that combines self-supervised MAE, semi-supervised hashtag classification, and CLIP-style contrastive learning to joi","authors_text":"Aashu Singh, Abhijeet Awasthi, Arkabandhu Chowdhury, Chaitanya Ahuja, Chao Li, David Jacobs, Don Husa, Hao Yuan, Haoyuan Xu, Hongli Xu, Hong-You Chen, Jianpeng Cheng, Jihye Moon, Jun Xiao, Linda Wang, Michael Ge, Michael Hsu, Phong Dingh, Qi Guo, Satya Narayan Shukla, Shlok Mishra, Sreya Dutta Roy, Sumedha Singla, Tsung-Yu Lin, Xiangjun Fan, Yimin Liu, Yonghuan Yang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-02-18T22:22:44Z","title":"Xray-Visual Models: Scaling Vision models on Industry Scale Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.16918","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f5214b25d89467a5ee76017de8c1807c6cca3a6ab871a4d283bb79167cabe76b","target":"record","created_at":"2026-07-15T00:21:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f55b84f8a9e8ed39df0527767569ad7849c1fb159b034b780d8c313c9bb45120","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-02-18T22:22:44Z","title_canon_sha256":"8d0572b7cd8f53e75751bee0d854b773a61c2a950897c7fa5d7b136e4ea56d90"},"schema_version":"1.0","source":{"id":"2602.16918","kind":"arxiv","version":2}},"canonical_sha256":"0f97ff3e678cd8744565d6d65db40dba3740b611c0d830ce5ba79bd70241d4ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f97ff3e678cd8744565d6d65db40dba3740b611c0d830ce5ba79bd70241d4ea","first_computed_at":"2026-07-15T00:21:16.876115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-15T00:21:16.876115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S2Q0B6uKwy/Iizv+wRJgLoTOQljYOcqnhKrAO7tNMLZlmGJbVMfXC8jYb20UkPG5X43xQnMwoGO7BrE86iF/Ag==","signature_status":"signed_v1","signed_at":"2026-07-15T00:21:16.877013Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.16918","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f5214b25d89467a5ee76017de8c1807c6cca3a6ab871a4d283bb79167cabe76b","sha256:5c69ee16df2e11e3e13b76dee10d189af59c9e99e0c7615f2b4a05adc50e1564"],"state_sha256":"39b913a4c5a80bb6aa734adffae18ab26d748cab1b4b8c53be446456f119721d"}