{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:FPYKJQQA6J3FEHORO6G4GZWCKJ","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":"e6a3279a3da55b5c0d689570dc62e3f6e3708c9e3733767cfa74fe8dcad52b54","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T16:12:50Z","title_canon_sha256":"7ca3169e80fcb323562decd564098f69887756f0ddab59d9cc9e09530abf71a2"},"schema_version":"1.0","source":{"id":"2605.16127","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16127","created_at":"2026-05-20T00:01:53Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16127v1","created_at":"2026-05-20T00:01:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16127","created_at":"2026-05-20T00:01:53Z"},{"alias_kind":"pith_short_12","alias_value":"FPYKJQQA6J3F","created_at":"2026-05-20T00:01:53Z"},{"alias_kind":"pith_short_16","alias_value":"FPYKJQQA6J3FEHOR","created_at":"2026-05-20T00:01:53Z"},{"alias_kind":"pith_short_8","alias_value":"FPYKJQQA","created_at":"2026-05-20T00:01:53Z"}],"graph_snapshots":[{"event_id":"sha256:ae771fa0ca152b2e5b0a9d4b66309c4632c07c2f62ef6819bbd24d1efd560e7d","target":"graph","created_at":"2026-05-20T00:01:53Z","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":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:33.380135Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.468277Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.16127/integrity.json","findings":[],"snapshot_sha256":"28e484a98165775ce05fc9a47a4895c8b1ca43a8f4d215062c0143727092e9d8","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While multi-modal 3D semantic occupancy prediction typically enhances robustness by fusing camera and LiDAR inputs, its effectiveness is fundamentally constrained by environmental variability. Specifically, camera sensors suffer from severe low-light degradation, while LiDAR sensors encounter significant backscatter noise during heavy precipitation. These adverse conditions create a modality trust problem, as static fusion strategies fail to adaptively re-weight inputs when a specific sensor becomes unreliable. To address this, we propose a VLM-assisted framework leveraging the pre-trained CLI","authors_text":"Abdelaziz Hussein, A. Enes Doruk, Hasan F. Ates","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T16:12:50Z","title":"WeatherOcc3D: VLM-Assisted Adverse Weather Aware 3D Semantic Occupancy Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16127","kind":"arxiv","version":1},"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":"sha256:38c2da5caf45a1c1a6f6dd82eac951521af1d1c83a899da017247494331e6733","target":"record","created_at":"2026-05-20T00:01:53Z","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":"e6a3279a3da55b5c0d689570dc62e3f6e3708c9e3733767cfa74fe8dcad52b54","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T16:12:50Z","title_canon_sha256":"7ca3169e80fcb323562decd564098f69887756f0ddab59d9cc9e09530abf71a2"},"schema_version":"1.0","source":{"id":"2605.16127","kind":"arxiv","version":1}},"canonical_sha256":"2bf0a4c200f276521dd1778dc366c2524d13bddbb2eacfb6f7e1710ef63c769b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2bf0a4c200f276521dd1778dc366c2524d13bddbb2eacfb6f7e1710ef63c769b","first_computed_at":"2026-05-20T00:01:53.991238Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:53.991238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V94kaofcpWToF/ioYrDU1j1SIJJVjniMIfBigY5gOR+ojzy+P7pF8xabmtnXuuL6/vMlzHf/jTXQSpPxmyx4Cw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:53.991829Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16127","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38c2da5caf45a1c1a6f6dd82eac951521af1d1c83a899da017247494331e6733","sha256:ae771fa0ca152b2e5b0a9d4b66309c4632c07c2f62ef6819bbd24d1efd560e7d"],"state_sha256":"b57f710810225e2e386fd1ba4c2acf3d755cd703faebff5543df7d2fc05f1eff"}