{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:JUYPKXAGFW3QPNPC6ZC3BSE3GK","short_pith_number":"pith:JUYPKXAG","canonical_record":{"source":{"id":"2606.04618","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-03T08:53:39Z","cross_cats_sorted":[],"title_canon_sha256":"3027193ffed25d4cebc3454ab9ed4d53202f6e78281532603d8a25184308c07b","abstract_canon_sha256":"9044f40f1296a4e0348e79adc559a0534ee4c9e6c611dbf6fd562d84298c5f8d"},"schema_version":"1.0"},"canonical_sha256":"4d30f55c062db707b5e2f645b0c89b3284eba2d544b10bbbe1cb753c83e3e5d2","source":{"kind":"arxiv","id":"2606.04618","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04618","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04618v1","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04618","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"pith_short_12","alias_value":"JUYPKXAGFW3Q","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"pith_short_16","alias_value":"JUYPKXAGFW3QPNPC","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"pith_short_8","alias_value":"JUYPKXAG","created_at":"2026-06-04T01:09:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:JUYPKXAGFW3QPNPC6ZC3BSE3GK","target":"record","payload":{"canonical_record":{"source":{"id":"2606.04618","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-03T08:53:39Z","cross_cats_sorted":[],"title_canon_sha256":"3027193ffed25d4cebc3454ab9ed4d53202f6e78281532603d8a25184308c07b","abstract_canon_sha256":"9044f40f1296a4e0348e79adc559a0534ee4c9e6c611dbf6fd562d84298c5f8d"},"schema_version":"1.0"},"canonical_sha256":"4d30f55c062db707b5e2f645b0c89b3284eba2d544b10bbbe1cb753c83e3e5d2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:09:21.572324Z","signature_b64":"oRqfo4Mltk7s92Zl7E+qQwdA0dac4t2nm0wx682Qp0FqkovBe42EUr1HPhX1ERJp3lVoh5Z/HJEFq2I3CGGFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d30f55c062db707b5e2f645b0c89b3284eba2d544b10bbbe1cb753c83e3e5d2","last_reissued_at":"2026-06-04T01:09:21.571558Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:09:21.571558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.04618","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":"2026-06-04T01:09:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CWv6AmL38Lpv2PVVGGoRBE+6EyZvc19hlPbAcRacDuQrpKUiVsVy3UVw5W0howPVcxhoOnlPN+VYsgw2FjoRDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T04:40:45.069775Z"},"content_sha256":"14c6522baefb6af1e94529ca5893ca1d41d0193e13bd598beca829dd0cac6500","schema_version":"1.0","event_id":"sha256:14c6522baefb6af1e94529ca5893ca1d41d0193e13bd598beca829dd0cac6500"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:JUYPKXAGFW3QPNPC6ZC3BSE3GK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Antonio Sgorbissa, Haolan Zhang, Nak Young Chong, Thanh Nguyen Canh, Xiem HoangVan","submitted_at":"2026-06-03T08:53:39Z","abstract_excerpt":"Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, recompute association weights as the map grows, or rely on hand-tuned null-hypothesis weights. To address these limitations, we propose \\textbf{BPDA-GMM}, an online Bayesian PDA framework for semantic SLAM with a growing object-level map. BPDA-GMM uses a Dirichlet-process prior to induce a Chinese Restaurant Process (CRP) association model, where accumulated evidence favors existing landmarks, and the concentration parameter assigns probability mas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04618","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/2606.04618/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":"2026-06-04T01:09:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pV92phyPQiJju4jFsCdsqi3Thud8X8DY+3ec2bd5QyBMqNsriyOTsDoZHSNUxZCq/Eig4F/yL7HXjYDb4MOCBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T04:40:45.070139Z"},"content_sha256":"7c0a30ebd3be68715b4e4a7c79d1eeb44e3c99290795df33ea3063ff45580a47","schema_version":"1.0","event_id":"sha256:7c0a30ebd3be68715b4e4a7c79d1eeb44e3c99290795df33ea3063ff45580a47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JUYPKXAGFW3QPNPC6ZC3BSE3GK/bundle.json","state_url":"https://pith.science/pith/JUYPKXAGFW3QPNPC6ZC3BSE3GK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JUYPKXAGFW3QPNPC6ZC3BSE3GK/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":"2026-06-28T04:40:45Z","links":{"resolver":"https://pith.science/pith/JUYPKXAGFW3QPNPC6ZC3BSE3GK","bundle":"https://pith.science/pith/JUYPKXAGFW3QPNPC6ZC3BSE3GK/bundle.json","state":"https://pith.science/pith/JUYPKXAGFW3QPNPC6ZC3BSE3GK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JUYPKXAGFW3QPNPC6ZC3BSE3GK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JUYPKXAGFW3QPNPC6ZC3BSE3GK","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":"9044f40f1296a4e0348e79adc559a0534ee4c9e6c611dbf6fd562d84298c5f8d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-03T08:53:39Z","title_canon_sha256":"3027193ffed25d4cebc3454ab9ed4d53202f6e78281532603d8a25184308c07b"},"schema_version":"1.0","source":{"id":"2606.04618","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04618","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04618v1","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04618","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"pith_short_12","alias_value":"JUYPKXAGFW3Q","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"pith_short_16","alias_value":"JUYPKXAGFW3QPNPC","created_at":"2026-06-04T01:09:21Z"},{"alias_kind":"pith_short_8","alias_value":"JUYPKXAG","created_at":"2026-06-04T01:09:21Z"}],"graph_snapshots":[{"event_id":"sha256:7c0a30ebd3be68715b4e4a7c79d1eeb44e3c99290795df33ea3063ff45580a47","target":"graph","created_at":"2026-06-04T01:09:21Z","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/2606.04618/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, recompute association weights as the map grows, or rely on hand-tuned null-hypothesis weights. To address these limitations, we propose \\textbf{BPDA-GMM}, an online Bayesian PDA framework for semantic SLAM with a growing object-level map. BPDA-GMM uses a Dirichlet-process prior to induce a Chinese Restaurant Process (CRP) association model, where accumulated evidence favors existing landmarks, and the concentration parameter assigns probability mas","authors_text":"Antonio Sgorbissa, Haolan Zhang, Nak Young Chong, Thanh Nguyen Canh, Xiem HoangVan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-03T08:53:39Z","title":"BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04618","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":"sha256:14c6522baefb6af1e94529ca5893ca1d41d0193e13bd598beca829dd0cac6500","target":"record","created_at":"2026-06-04T01:09:21Z","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":"9044f40f1296a4e0348e79adc559a0534ee4c9e6c611dbf6fd562d84298c5f8d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-03T08:53:39Z","title_canon_sha256":"3027193ffed25d4cebc3454ab9ed4d53202f6e78281532603d8a25184308c07b"},"schema_version":"1.0","source":{"id":"2606.04618","kind":"arxiv","version":1}},"canonical_sha256":"4d30f55c062db707b5e2f645b0c89b3284eba2d544b10bbbe1cb753c83e3e5d2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d30f55c062db707b5e2f645b0c89b3284eba2d544b10bbbe1cb753c83e3e5d2","first_computed_at":"2026-06-04T01:09:21.571558Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:09:21.571558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oRqfo4Mltk7s92Zl7E+qQwdA0dac4t2nm0wx682Qp0FqkovBe42EUr1HPhX1ERJp3lVoh5Z/HJEFq2I3CGGFBA==","signature_status":"signed_v1","signed_at":"2026-06-04T01:09:21.572324Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.04618","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14c6522baefb6af1e94529ca5893ca1d41d0193e13bd598beca829dd0cac6500","sha256:7c0a30ebd3be68715b4e4a7c79d1eeb44e3c99290795df33ea3063ff45580a47"],"state_sha256":"6a7cd904bf4d0b2ff19cc7435918fee0dd3148be96a34975a0c5922550f6ad87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"11usQ7sBnsNIM3y9OJOKVteXGXKZlBUdsttv3F49Z/WZCFjvPSfONX8cbMP+J4dUKDxlYjLRu/ob9HU3hIQdCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T04:40:45.072118Z","bundle_sha256":"173347c6f4ad99d7d7479745544140b374d2c63f33c7acaf673ca522d6702d4f"}}