{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EUBTZOBUUBBCNMSVEC7X6YI2VU","short_pith_number":"pith:EUBTZOBU","canonical_record":{"source":{"id":"2410.12673","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-16T15:37:29Z","cross_cats_sorted":[],"title_canon_sha256":"639d7742345f91acf196ecc91de12c71f2963850596832f1c37926e56da5ed87","abstract_canon_sha256":"be31b9e7857fd078d0d919fe10994f43cdfd980a060ae96bbbd5f2bd9166ec91"},"schema_version":"1.0"},"canonical_sha256":"25033cb834a04226b25520bf7f611aad320a97f016945e548ba7adfd93e8ccc5","source":{"kind":"arxiv","id":"2410.12673","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.12673","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"arxiv_version","alias_value":"2410.12673v3","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12673","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"pith_short_12","alias_value":"EUBTZOBUUBBC","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"pith_short_16","alias_value":"EUBTZOBUUBBCNMSV","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"pith_short_8","alias_value":"EUBTZOBU","created_at":"2026-05-26T01:03:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EUBTZOBUUBBCNMSVEC7X6YI2VU","target":"record","payload":{"canonical_record":{"source":{"id":"2410.12673","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-16T15:37:29Z","cross_cats_sorted":[],"title_canon_sha256":"639d7742345f91acf196ecc91de12c71f2963850596832f1c37926e56da5ed87","abstract_canon_sha256":"be31b9e7857fd078d0d919fe10994f43cdfd980a060ae96bbbd5f2bd9166ec91"},"schema_version":"1.0"},"canonical_sha256":"25033cb834a04226b25520bf7f611aad320a97f016945e548ba7adfd93e8ccc5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:09.415003Z","signature_b64":"zJCLVHAO4NezrFGq+pw1EgTW/pDDWbPrrtfm2exBFg80JunjHo4wCm+Zc/AhxUviTruAsU6sfsAR9ZGaK6fGCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25033cb834a04226b25520bf7f611aad320a97f016945e548ba7adfd93e8ccc5","last_reissued_at":"2026-05-26T01:03:09.414124Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:09.414124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.12673","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:03:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m98euqHwyrwas8EN/qs3S8rGHViliMJwc2PPZ8YL702DeJQxUEqYiM5SIztFSWKyf718tEtHDoAGAoSP/m69DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T17:31:32.416564Z"},"content_sha256":"d56b469b127b38f58b7809e306273997d6ba4ee36456f87f68ee730da485844c","schema_version":"1.0","event_id":"sha256:d56b469b127b38f58b7809e306273997d6ba4ee36456f87f68ee730da485844c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EUBTZOBUUBBCNMSVEC7X6YI2VU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MambaBEV: An EV-based 3D detection model with Mamba2","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Wang, Jinxiang Wang, Ni Wang, Qichao Zhao, Zihan You","submitted_at":"2024-10-16T15:37:29Z","abstract_excerpt":"Accurate 3D object detection in autonomous driving relies on Bird's Eye View (BEV) perception and effective temporal fusion. However, existing fusion strategies based on convolutional layers or deformable self-attention struggle to model global context in BEV space, leading to reduced accuracy for large objects.To address this limitation, we propose MambaBEV, a novel BEV-based 3D object detection model that leverages Mamba2, an advanced state-space model (SSM) optimized for long-sequence processing. Our key contribution is TemporalMamba, a temporal fusion module that enhances global context mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12673","kind":"arxiv","version":3},"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/2410.12673/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T01:03:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WlHMxB1P8NzW+UXIrQqZ6bxiY2WYoQJSB4YPsFIPH/Ci2iSWoZ8EKPR5fX0me6dVDDaNcYarBNTfJA9649e7Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T17:31:32.416938Z"},"content_sha256":"72567623f7ae8109fa2277c982517de12ed95a679288f49546d6b4a11757392a","schema_version":"1.0","event_id":"sha256:72567623f7ae8109fa2277c982517de12ed95a679288f49546d6b4a11757392a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EUBTZOBUUBBCNMSVEC7X6YI2VU/bundle.json","state_url":"https://pith.science/pith/EUBTZOBUUBBCNMSVEC7X6YI2VU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EUBTZOBUUBBCNMSVEC7X6YI2VU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-01T17:31:32Z","links":{"resolver":"https://pith.science/pith/EUBTZOBUUBBCNMSVEC7X6YI2VU","bundle":"https://pith.science/pith/EUBTZOBUUBBCNMSVEC7X6YI2VU/bundle.json","state":"https://pith.science/pith/EUBTZOBUUBBCNMSVEC7X6YI2VU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EUBTZOBUUBBCNMSVEC7X6YI2VU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EUBTZOBUUBBCNMSVEC7X6YI2VU","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":"be31b9e7857fd078d0d919fe10994f43cdfd980a060ae96bbbd5f2bd9166ec91","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-16T15:37:29Z","title_canon_sha256":"639d7742345f91acf196ecc91de12c71f2963850596832f1c37926e56da5ed87"},"schema_version":"1.0","source":{"id":"2410.12673","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.12673","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"arxiv_version","alias_value":"2410.12673v3","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12673","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"pith_short_12","alias_value":"EUBTZOBUUBBC","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"pith_short_16","alias_value":"EUBTZOBUUBBCNMSV","created_at":"2026-05-26T01:03:09Z"},{"alias_kind":"pith_short_8","alias_value":"EUBTZOBU","created_at":"2026-05-26T01:03:09Z"}],"graph_snapshots":[{"event_id":"sha256:72567623f7ae8109fa2277c982517de12ed95a679288f49546d6b4a11757392a","target":"graph","created_at":"2026-05-26T01:03:09Z","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/2410.12673/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate 3D object detection in autonomous driving relies on Bird's Eye View (BEV) perception and effective temporal fusion. However, existing fusion strategies based on convolutional layers or deformable self-attention struggle to model global context in BEV space, leading to reduced accuracy for large objects.To address this limitation, we propose MambaBEV, a novel BEV-based 3D object detection model that leverages Mamba2, an advanced state-space model (SSM) optimized for long-sequence processing. Our key contribution is TemporalMamba, a temporal fusion module that enhances global context mo","authors_text":"Hao Wang, Jinxiang Wang, Ni Wang, Qichao Zhao, Zihan You","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-16T15:37:29Z","title":"MambaBEV: An EV-based 3D detection model with Mamba2"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12673","kind":"arxiv","version":3},"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:d56b469b127b38f58b7809e306273997d6ba4ee36456f87f68ee730da485844c","target":"record","created_at":"2026-05-26T01:03:09Z","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":"be31b9e7857fd078d0d919fe10994f43cdfd980a060ae96bbbd5f2bd9166ec91","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-16T15:37:29Z","title_canon_sha256":"639d7742345f91acf196ecc91de12c71f2963850596832f1c37926e56da5ed87"},"schema_version":"1.0","source":{"id":"2410.12673","kind":"arxiv","version":3}},"canonical_sha256":"25033cb834a04226b25520bf7f611aad320a97f016945e548ba7adfd93e8ccc5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"25033cb834a04226b25520bf7f611aad320a97f016945e548ba7adfd93e8ccc5","first_computed_at":"2026-05-26T01:03:09.414124Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:03:09.414124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zJCLVHAO4NezrFGq+pw1EgTW/pDDWbPrrtfm2exBFg80JunjHo4wCm+Zc/AhxUviTruAsU6sfsAR9ZGaK6fGCA==","signature_status":"signed_v1","signed_at":"2026-05-26T01:03:09.415003Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.12673","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d56b469b127b38f58b7809e306273997d6ba4ee36456f87f68ee730da485844c","sha256:72567623f7ae8109fa2277c982517de12ed95a679288f49546d6b4a11757392a"],"state_sha256":"003b81719ca9d5dd999f0bb7216b184f868d6741cd11bec2a514e05b94dcfa02"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dbREpCOvz9bZPAzNrELNURdCcfcUP9Hsh+sDbVSkO+7p3gYHYetXWTRS6LLtBpAL6QEVEj8CfRCJ6krqyAU4Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T17:31:32.418873Z","bundle_sha256":"91a5400b2c004d42d8b1cf9664dea82a5346cec3c3199c683a16dff31becebbb"}}