{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AY6ZRKUPJKNOOPSS2L3EZ2N6S6","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":"8c25be4d486af609f6962fa3eb52c5571ce17cc80d2e4c72e2f0149f8cb8e797","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T11:47:02Z","title_canon_sha256":"738fe436920f1eb06872a735d3fe4b1325b68b8159aa25e206c6160e5d1607e9"},"schema_version":"1.0","source":{"id":"2605.31177","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.31177","created_at":"2026-06-01T01:04:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.31177v1","created_at":"2026-06-01T01:04:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31177","created_at":"2026-06-01T01:04:02Z"},{"alias_kind":"pith_short_12","alias_value":"AY6ZRKUPJKNO","created_at":"2026-06-01T01:04:02Z"},{"alias_kind":"pith_short_16","alias_value":"AY6ZRKUPJKNOOPSS","created_at":"2026-06-01T01:04:02Z"},{"alias_kind":"pith_short_8","alias_value":"AY6ZRKUP","created_at":"2026-06-01T01:04:02Z"}],"graph_snapshots":[{"event_id":"sha256:e0320e626415129fb7db48b8bf0c38109c35721fd14bfd03e3308ba9ae961681","target":"graph","created_at":"2026-06-01T01:04:02Z","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/2605.31177/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Plain Transformers have become the de-facto architecture for processing text, audio, image, and video, offering a unified backbone for multimodal learning. However, state-of-the-art architectures for point cloud semantic segmentation remain dominated by U-Nets architectures where convolutions are interleaved with local or windowed attentions. In this work, we show how to effectively leverage vanilla, non-hierarchical ViTs for segmentation of large-scale automotive lidar scenes. We bridge the performance gap thanks to a carefully designed tokenizer, a lightweight decoder segmentation head, and ","authors_text":"Alexandre Boulch, Gilles Puy, Nermin Samet, Renaud Marlet, Spyros Gidaris, Tuan-Hung Vu","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31177","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":"sha256:9416cf99632c90595450051da99370b92f90cac40f61e50c53a8739c5a5beb63","target":"record","created_at":"2026-06-01T01:04:02Z","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":"8c25be4d486af609f6962fa3eb52c5571ce17cc80d2e4c72e2f0149f8cb8e797","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T11:47:02Z","title_canon_sha256":"738fe436920f1eb06872a735d3fe4b1325b68b8159aa25e206c6160e5d1607e9"},"schema_version":"1.0","source":{"id":"2605.31177","kind":"arxiv","version":1}},"canonical_sha256":"063d98aa8f4a9ae73e52d2f64ce9be97827444c5278b41982d8363518d3089b2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"063d98aa8f4a9ae73e52d2f64ce9be97827444c5278b41982d8363518d3089b2","first_computed_at":"2026-06-01T01:04:02.625595Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T01:04:02.625595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TMWbo8FWb2KGTXxzuTxG1TiI5A1VHJ+i4seYh1tbbx/aGBK7B24fWpLOh2q3ZELxxMsza8vIJN1zwP2di6W1Bw==","signature_status":"signed_v1","signed_at":"2026-06-01T01:04:02.626205Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.31177","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9416cf99632c90595450051da99370b92f90cac40f61e50c53a8739c5a5beb63","sha256:e0320e626415129fb7db48b8bf0c38109c35721fd14bfd03e3308ba9ae961681"],"state_sha256":"d9c141f12b8810d44468edc418e4a226c368929ba03536470e49f8dfea80b7a0"}