{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:BPLFIZV7AWBDTD2ZKFNRAGJT7J","short_pith_number":"pith:BPLFIZV7","canonical_record":{"source":{"id":"2605.01320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-02T08:36:11Z","cross_cats_sorted":[],"title_canon_sha256":"b90248a27a4d027293ea5e7587738781c07f0923cc4e661dadff94001fbfb825","abstract_canon_sha256":"3d77959725d138e327b83f9f214f9100ad9e9d0c00d6da5be2de20f3e1ca622d"},"schema_version":"1.0"},"canonical_sha256":"0bd65466bf0582398f59515b101933fa54ec82e5ba7ce3f2a9a99bb2af18c1d8","source":{"kind":"arxiv","id":"2605.01320","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.01320","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"arxiv_version","alias_value":"2605.01320v2","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.01320","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"pith_short_12","alias_value":"BPLFIZV7AWBD","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"pith_short_16","alias_value":"BPLFIZV7AWBDTD2Z","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"pith_short_8","alias_value":"BPLFIZV7","created_at":"2026-06-09T01:05:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:BPLFIZV7AWBDTD2ZKFNRAGJT7J","target":"record","payload":{"canonical_record":{"source":{"id":"2605.01320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-02T08:36:11Z","cross_cats_sorted":[],"title_canon_sha256":"b90248a27a4d027293ea5e7587738781c07f0923cc4e661dadff94001fbfb825","abstract_canon_sha256":"3d77959725d138e327b83f9f214f9100ad9e9d0c00d6da5be2de20f3e1ca622d"},"schema_version":"1.0"},"canonical_sha256":"0bd65466bf0582398f59515b101933fa54ec82e5ba7ce3f2a9a99bb2af18c1d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:18.566902Z","signature_b64":"dn/y0cZl13CNJYpK206tbpwiYGIBUMEGtsbUujmycYjA4yzpKbSe5gmvkfrB4XE0AgHN43ZPBl+1rJdmzJ1dCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bd65466bf0582398f59515b101933fa54ec82e5ba7ce3f2a9a99bb2af18c1d8","last_reissued_at":"2026-06-09T01:05:18.566479Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:18.566479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.01320","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NnOkh904OaL0Ne5vG8e4wp7v2bYX2Ni6FLmnhke8XUrVKHmLeRXRavP0sdyr2ls7Vv8jqfCzPmVlVbNbj1rfBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T11:11:24.891428Z"},"content_sha256":"20f76f573792a4acbf5b1e448f899599d907a9c3460d58fa32872ab2a8ce1c80","schema_version":"1.0","event_id":"sha256:20f76f573792a4acbf5b1e448f899599d907a9c3460d58fa32872ab2a8ce1c80"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:BPLFIZV7AWBDTD2ZKFNRAGJT7J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dandan Ding, Jiahao Zhu, Kang You, Zhan Ma","submitted_at":"2026-05-02T08:36:11Z","abstract_excerpt":"LiDAR point cloud compression is vital for autonomous systems to handle massive data from high-resolution sensors. While learned entropy modeling built upon octree structures yields high compression gains, it faces two critical bottlenecks: 1) prohibitive latency, particularly during decoding, caused by causal, multi-stage context modeling; and 2) a rigid performance-latency trade-off, preventing a single model from adapting to varying constraints. These limitations stem from the tight coupling between the context aggregation backbone and probability prediction. To address this, we propose PAC"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"PACE sets a new state-of-the-art in compression efficiency, achieving notable BD-BR savings and reducing decoding latency by over 90% in autoregressive mode.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The non-causal backbone can still provide sufficient context for accurate probability prediction when causality is confined to the lightweight predictor, without loss of modeling power from the original tight coupling.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4ef7ebd58a9e0ada4fa006978eb478084e55479d86473d26d064482a80a31bbf"},"source":{"id":"2605.01320","kind":"arxiv","version":2},"verdict":{"id":"979c80f0-1f05-4c1d-be0b-9a2af5d019ee","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T14:31:33.574342Z","strongest_claim":"PACE sets a new state-of-the-art in compression efficiency, achieving notable BD-BR savings and reducing decoding latency by over 90% in autoregressive mode.","one_line_summary":"PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The non-causal backbone can still provide sufficient context for accurate probability prediction when causality is confined to the lightweight predictor, without loss of modeling power from the original tight coupling.","pith_extraction_headline":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.01320/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T18:35:15.090729Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T17:21:21.493926Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ff8f8f50c77f4f0f3adeec4e21ebe220699232540e66361156cfb6a732bc0e48"},"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":"979c80f0-1f05-4c1d-be0b-9a2af5d019ee"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cbUTmBavy4WYH5N6OGY448LV4q3jjgb5veEdXAb1bBCvdjk1TiY8ZhZDF206se60McwvERS+naFNNxHiwQNQBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T11:11:24.891902Z"},"content_sha256":"40866eeb4a989c0fe270374170a03bd25eafd598828e7ed8eac6d13f2e9d5e2c","schema_version":"1.0","event_id":"sha256:40866eeb4a989c0fe270374170a03bd25eafd598828e7ed8eac6d13f2e9d5e2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J/bundle.json","state_url":"https://pith.science/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J/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":"2026-06-09T11:11:24Z","links":{"resolver":"https://pith.science/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J","bundle":"https://pith.science/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J/bundle.json","state":"https://pith.science/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BPLFIZV7AWBDTD2ZKFNRAGJT7J","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":"3d77959725d138e327b83f9f214f9100ad9e9d0c00d6da5be2de20f3e1ca622d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-02T08:36:11Z","title_canon_sha256":"b90248a27a4d027293ea5e7587738781c07f0923cc4e661dadff94001fbfb825"},"schema_version":"1.0","source":{"id":"2605.01320","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.01320","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"arxiv_version","alias_value":"2605.01320v2","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.01320","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"pith_short_12","alias_value":"BPLFIZV7AWBD","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"pith_short_16","alias_value":"BPLFIZV7AWBDTD2Z","created_at":"2026-06-09T01:05:18Z"},{"alias_kind":"pith_short_8","alias_value":"BPLFIZV7","created_at":"2026-06-09T01:05:18Z"}],"graph_snapshots":[{"event_id":"sha256:40866eeb4a989c0fe270374170a03bd25eafd598828e7ed8eac6d13f2e9d5e2c","target":"graph","created_at":"2026-06-09T01:05:18Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"PACE sets a new state-of-the-art in compression efficiency, achieving notable BD-BR savings and reducing decoding latency by over 90% in autoregressive mode."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The non-causal backbone can still provide sufficient context for accurate probability prediction when causality is confined to the lightweight predictor, without loss of modeling power from the original tight coupling."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression."}],"snapshot_sha256":"4ef7ebd58a9e0ada4fa006978eb478084e55479d86473d26d064482a80a31bbf"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T18:35:15.090729Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T17:21:21.493926Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.01320/integrity.json","findings":[],"snapshot_sha256":"ff8f8f50c77f4f0f3adeec4e21ebe220699232540e66361156cfb6a732bc0e48","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"LiDAR point cloud compression is vital for autonomous systems to handle massive data from high-resolution sensors. While learned entropy modeling built upon octree structures yields high compression gains, it faces two critical bottlenecks: 1) prohibitive latency, particularly during decoding, caused by causal, multi-stage context modeling; and 2) a rigid performance-latency trade-off, preventing a single model from adapting to varying constraints. These limitations stem from the tight coupling between the context aggregation backbone and probability prediction. To address this, we propose PAC","authors_text":"Dandan Ding, Jiahao Zhu, Kang You, Zhan Ma","cross_cats":[],"headline":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-02T08:36:11Z","title":"PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.01320","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-09T14:31:33.574342Z","id":"979c80f0-1f05-4c1d-be0b-9a2af5d019ee","model_set":{"reader":"grok-4.3"},"one_line_summary":"PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression.","strongest_claim":"PACE sets a new state-of-the-art in compression efficiency, achieving notable BD-BR savings and reducing decoding latency by over 90% in autoregressive mode.","weakest_assumption":"The non-causal backbone can still provide sufficient context for accurate probability prediction when causality is confined to the lightweight predictor, without loss of modeling power from the original tight coupling."}},"verdict_id":"979c80f0-1f05-4c1d-be0b-9a2af5d019ee"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:20f76f573792a4acbf5b1e448f899599d907a9c3460d58fa32872ab2a8ce1c80","target":"record","created_at":"2026-06-09T01:05:18Z","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":"3d77959725d138e327b83f9f214f9100ad9e9d0c00d6da5be2de20f3e1ca622d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-02T08:36:11Z","title_canon_sha256":"b90248a27a4d027293ea5e7587738781c07f0923cc4e661dadff94001fbfb825"},"schema_version":"1.0","source":{"id":"2605.01320","kind":"arxiv","version":2}},"canonical_sha256":"0bd65466bf0582398f59515b101933fa54ec82e5ba7ce3f2a9a99bb2af18c1d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0bd65466bf0582398f59515b101933fa54ec82e5ba7ce3f2a9a99bb2af18c1d8","first_computed_at":"2026-06-09T01:05:18.566479Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:18.566479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dn/y0cZl13CNJYpK206tbpwiYGIBUMEGtsbUujmycYjA4yzpKbSe5gmvkfrB4XE0AgHN43ZPBl+1rJdmzJ1dCw==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:18.566902Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.01320","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20f76f573792a4acbf5b1e448f899599d907a9c3460d58fa32872ab2a8ce1c80","sha256:40866eeb4a989c0fe270374170a03bd25eafd598828e7ed8eac6d13f2e9d5e2c"],"state_sha256":"75376be43dbdfa160fd51a721d232748de3425ca7354a3ce23d65be2d324d34c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/4Ex28URa42unQfn+4/YoHF4n385EsVYOfeXi84+fMPwou/Wfic6kw3ibNaxM6k6p47Ek6h52Az5Q3Asys9PDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T11:11:24.894097Z","bundle_sha256":"7db966dc3c86cf037bf6cf5395ffa3a4d23fe70572b7119971324bf22987bb11"}}