{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4PYWXD5NNZXYXLRFN3QGYENVFZ","short_pith_number":"pith:4PYWXD5N","canonical_record":{"source":{"id":"2506.10242","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T23:49:56Z","cross_cats_sorted":[],"title_canon_sha256":"bff5535e536b5af2a47ed948582398f44d2412af804e609f6b41b94e9c6cf962","abstract_canon_sha256":"ad26b4da8279357202a9acacc2301c00d9c1994c904afbc21f7d16feae8ae074"},"schema_version":"1.0"},"canonical_sha256":"e3f16b8fad6e6f8bae256ee06c11b52e759a4053a4bf05f476f79c06c015ca6a","source":{"kind":"arxiv","id":"2506.10242","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.10242","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"2506.10242v1","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10242","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"4PYWXD5NNZXY","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"pith_short_16","alias_value":"4PYWXD5NNZXYXLRF","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"pith_short_8","alias_value":"4PYWXD5N","created_at":"2026-07-05T11:20:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4PYWXD5NNZXYXLRFN3QGYENVFZ","target":"record","payload":{"canonical_record":{"source":{"id":"2506.10242","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T23:49:56Z","cross_cats_sorted":[],"title_canon_sha256":"bff5535e536b5af2a47ed948582398f44d2412af804e609f6b41b94e9c6cf962","abstract_canon_sha256":"ad26b4da8279357202a9acacc2301c00d9c1994c904afbc21f7d16feae8ae074"},"schema_version":"1.0"},"canonical_sha256":"e3f16b8fad6e6f8bae256ee06c11b52e759a4053a4bf05f476f79c06c015ca6a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:19.703073Z","signature_b64":"ViPFyVjdw63s2uNNwjfWZQwn3tfYLWnAnXhPqkRknlvoDkOULrnk0FjyEYC3T2xdvMEa56skzYdH8n4VYR0xDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3f16b8fad6e6f8bae256ee06c11b52e759a4053a4bf05f476f79c06c015ca6a","last_reissued_at":"2026-07-05T11:20:19.702598Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:19.702598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.10242","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5sb/in73ngq6iR0H19Ykt77EqekW/9oUYRvZ7JqzNdmby5hkP455b0+/StGwr74JKXqQ0/L3TnufQxSiMvrpBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:06:53.742691Z"},"content_sha256":"c39826f2e5e25aa67dd277431a06fbd2ff7288f21535c3224f5fb2766f2b2e84","schema_version":"1.0","event_id":"sha256:c39826f2e5e25aa67dd277431a06fbd2ff7288f21535c3224f5fb2766f2b2e84"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4PYWXD5NNZXYXLRFN3QGYENVFZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fatih Porikli, Hong Cai, Rajeev Yasarla, Shizhong Han","submitted_at":"2025-06-11T23:49:56Z","abstract_excerpt":"Camera-based 3D object detection in Bird's Eye View (BEV) is one of the most important perception tasks in autonomous driving. Earlier methods rely on dense BEV features, which are costly to construct. More recent works explore sparse query-based detection. However, they still require a large number of queries and can become expensive to run when more video frames are used. In this paper, we propose DySS, a novel method that employs state-space learning and dynamic queries. More specifically, DySS leverages a state-space model (SSM) to sequentially process the sampled features over time steps."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10242","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/2506.10242/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":"2026-07-05T11:20:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LiiSFWnPvtwGSwYbBw2aezlcaOQ8TDDn3sjb5hErIAG/5BxwvNvZm9Tv0MHKvQYZ0EmdobUr7x3ynEWBWLa6Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:06:53.743179Z"},"content_sha256":"2af81d898125f643065d8fa765c983cad393054ef76b99498f630ae7e564aaad","schema_version":"1.0","event_id":"sha256:2af81d898125f643065d8fa765c983cad393054ef76b99498f630ae7e564aaad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4PYWXD5NNZXYXLRFN3QGYENVFZ/bundle.json","state_url":"https://pith.science/pith/4PYWXD5NNZXYXLRFN3QGYENVFZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4PYWXD5NNZXYXLRFN3QGYENVFZ/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":"2026-08-08T22:06:53Z","links":{"resolver":"https://pith.science/pith/4PYWXD5NNZXYXLRFN3QGYENVFZ","bundle":"https://pith.science/pith/4PYWXD5NNZXYXLRFN3QGYENVFZ/bundle.json","state":"https://pith.science/pith/4PYWXD5NNZXYXLRFN3QGYENVFZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4PYWXD5NNZXYXLRFN3QGYENVFZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4PYWXD5NNZXYXLRFN3QGYENVFZ","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":"ad26b4da8279357202a9acacc2301c00d9c1994c904afbc21f7d16feae8ae074","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T23:49:56Z","title_canon_sha256":"bff5535e536b5af2a47ed948582398f44d2412af804e609f6b41b94e9c6cf962"},"schema_version":"1.0","source":{"id":"2506.10242","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.10242","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"arxiv_version","alias_value":"2506.10242v1","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.10242","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"pith_short_12","alias_value":"4PYWXD5NNZXY","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"pith_short_16","alias_value":"4PYWXD5NNZXYXLRF","created_at":"2026-07-05T11:20:19Z"},{"alias_kind":"pith_short_8","alias_value":"4PYWXD5N","created_at":"2026-07-05T11:20:19Z"}],"graph_snapshots":[{"event_id":"sha256:2af81d898125f643065d8fa765c983cad393054ef76b99498f630ae7e564aaad","target":"graph","created_at":"2026-07-05T11:20:19Z","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/2506.10242/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Camera-based 3D object detection in Bird's Eye View (BEV) is one of the most important perception tasks in autonomous driving. Earlier methods rely on dense BEV features, which are costly to construct. More recent works explore sparse query-based detection. However, they still require a large number of queries and can become expensive to run when more video frames are used. In this paper, we propose DySS, a novel method that employs state-space learning and dynamic queries. More specifically, DySS leverages a state-space model (SSM) to sequentially process the sampled features over time steps.","authors_text":"Fatih Porikli, Hong Cai, Rajeev Yasarla, Shizhong Han","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T23:49:56Z","title":"DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.10242","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:c39826f2e5e25aa67dd277431a06fbd2ff7288f21535c3224f5fb2766f2b2e84","target":"record","created_at":"2026-07-05T11:20:19Z","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":"ad26b4da8279357202a9acacc2301c00d9c1994c904afbc21f7d16feae8ae074","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T23:49:56Z","title_canon_sha256":"bff5535e536b5af2a47ed948582398f44d2412af804e609f6b41b94e9c6cf962"},"schema_version":"1.0","source":{"id":"2506.10242","kind":"arxiv","version":1}},"canonical_sha256":"e3f16b8fad6e6f8bae256ee06c11b52e759a4053a4bf05f476f79c06c015ca6a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e3f16b8fad6e6f8bae256ee06c11b52e759a4053a4bf05f476f79c06c015ca6a","first_computed_at":"2026-07-05T11:20:19.702598Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:20:19.702598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ViPFyVjdw63s2uNNwjfWZQwn3tfYLWnAnXhPqkRknlvoDkOULrnk0FjyEYC3T2xdvMEa56skzYdH8n4VYR0xDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:20:19.703073Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.10242","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c39826f2e5e25aa67dd277431a06fbd2ff7288f21535c3224f5fb2766f2b2e84","sha256:2af81d898125f643065d8fa765c983cad393054ef76b99498f630ae7e564aaad"],"state_sha256":"febb7786c8c90372214437aacc0d06e90acf0375cf547e9c1d89db9c8a156a3c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G0eS0VyOIMvtLV4BluVIExOpk+aCIzbddwfJ9BnSuBnnp6y6kHmAdQ5nvu5g2WRx5LHzwCdqZuyC5P0XvEE2Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:06:53.748212Z","bundle_sha256":"15f108a2890f1a53423eda3e34981eb92155edc24177bb504cec099ac9dfee88"}}