{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TX6XBCYLJLXPWMNIAVIHH3NPUI","short_pith_number":"pith:TX6XBCYL","canonical_record":{"source":{"id":"2607.26763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T11:04:01Z","cross_cats_sorted":[],"title_canon_sha256":"28a29f9e9e7087b513459acf99902fca661f318cae765311f94aa8eb704781c5","abstract_canon_sha256":"775a59533f848306ad8e4e1ebe3742026464571fd06fc884adfe3e248dddc26b"},"schema_version":"1.0"},"canonical_sha256":"9dfd708b0b4aeefb31a8055073edafa2049b09262adf89766b40ea51145b939d","source":{"kind":"arxiv","id":"2607.26763","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.26763","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"arxiv_version","alias_value":"2607.26763v1","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26763","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"pith_short_12","alias_value":"TX6XBCYLJLXP","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"pith_short_16","alias_value":"TX6XBCYLJLXPWMNI","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"pith_short_8","alias_value":"TX6XBCYL","created_at":"2026-07-30T01:22:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TX6XBCYLJLXPWMNIAVIHH3NPUI","target":"record","payload":{"canonical_record":{"source":{"id":"2607.26763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T11:04:01Z","cross_cats_sorted":[],"title_canon_sha256":"28a29f9e9e7087b513459acf99902fca661f318cae765311f94aa8eb704781c5","abstract_canon_sha256":"775a59533f848306ad8e4e1ebe3742026464571fd06fc884adfe3e248dddc26b"},"schema_version":"1.0"},"canonical_sha256":"9dfd708b0b4aeefb31a8055073edafa2049b09262adf89766b40ea51145b939d","receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9dfd708b0b4aeefb31a8055073edafa2049b09262adf89766b40ea51145b939d","last_reissued_at":"2026-07-30T01:22:13.483857Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:22:13.483857Z"},"source_kind":"arxiv","source_id":"2607.26763","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":"2026-07-30T01:22:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8C19uGnLur7LAF9pIYpniAOxSb6wGB/7RboWVOJTxIxLg/yGVhUDGm3han7mT2ombVMmiNHw2pabG+CZqCwgBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T16:32:42.357717Z"},"content_sha256":"700e88030dce9948054bd9ee4a31d92ff9e399054a03a1749572a3d068ca7e33","schema_version":"1.0","event_id":"sha256:700e88030dce9948054bd9ee4a31d92ff9e399054a03a1749572a3d068ca7e33"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TX6XBCYLJLXPWMNIAVIHH3NPUI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Long-Tailed 3D Point Cloud Dataset Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiahao You, Jinfeng Xu, Xianzhi Li, Xu Han","submitted_at":"2026-07-29T11:04:01Z","abstract_excerpt":"Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving their training utility, enabling efficient 3D point cloud training. Current point cloud dataset distillation methods only tackle geometric and representation challenges while ignoring the distributional imbalance prevalent in point cloud datasets where both training and test splits follow long-tailed class distributions. To our knowledge, we present the first study on long-tailed point cloud dataset distillation. Rather than focusing primarily on geometric and representation properties or simply c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26763","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/2607.26763/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":"2026-07-30T01:22:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rAdbS7EkqZ/50CumaIJGmNo/KniujdP+8Y+GZQuvKpeINSEUTb3lou3vEHO1KGGj3JiG+jLeQRdBaEuNp8g/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T16:32:42.358637Z"},"content_sha256":"ac7a2eeb3198c004fcc2d0c59d5af65dc4fbb05c49903c133998ff925503760b","schema_version":"1.0","event_id":"sha256:ac7a2eeb3198c004fcc2d0c59d5af65dc4fbb05c49903c133998ff925503760b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TX6XBCYLJLXPWMNIAVIHH3NPUI/bundle.json","state_url":"https://pith.science/pith/TX6XBCYLJLXPWMNIAVIHH3NPUI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TX6XBCYLJLXPWMNIAVIHH3NPUI/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":"2026-08-15T16:32:42Z","links":{"resolver":"https://pith.science/pith/TX6XBCYLJLXPWMNIAVIHH3NPUI","bundle":"https://pith.science/pith/TX6XBCYLJLXPWMNIAVIHH3NPUI/bundle.json","state":"https://pith.science/pith/TX6XBCYLJLXPWMNIAVIHH3NPUI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TX6XBCYLJLXPWMNIAVIHH3NPUI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TX6XBCYLJLXPWMNIAVIHH3NPUI","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":"775a59533f848306ad8e4e1ebe3742026464571fd06fc884adfe3e248dddc26b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T11:04:01Z","title_canon_sha256":"28a29f9e9e7087b513459acf99902fca661f318cae765311f94aa8eb704781c5"},"schema_version":"1.0","source":{"id":"2607.26763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.26763","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"arxiv_version","alias_value":"2607.26763v1","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26763","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"pith_short_12","alias_value":"TX6XBCYLJLXP","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"pith_short_16","alias_value":"TX6XBCYLJLXPWMNI","created_at":"2026-07-30T01:22:13Z"},{"alias_kind":"pith_short_8","alias_value":"TX6XBCYL","created_at":"2026-07-30T01:22:13Z"}],"graph_snapshots":[{"event_id":"sha256:ac7a2eeb3198c004fcc2d0c59d5af65dc4fbb05c49903c133998ff925503760b","target":"graph","created_at":"2026-07-30T01:22:13Z","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/2607.26763/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving their training utility, enabling efficient 3D point cloud training. Current point cloud dataset distillation methods only tackle geometric and representation challenges while ignoring the distributional imbalance prevalent in point cloud datasets where both training and test splits follow long-tailed class distributions. To our knowledge, we present the first study on long-tailed point cloud dataset distillation. Rather than focusing primarily on geometric and representation properties or simply c","authors_text":"Jiahao You, Jinfeng Xu, Xianzhi Li, Xu Han","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T11:04:01Z","title":"Long-Tailed 3D Point Cloud Dataset Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26763","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":"sha256:700e88030dce9948054bd9ee4a31d92ff9e399054a03a1749572a3d068ca7e33","target":"record","created_at":"2026-07-30T01:22:13Z","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":"775a59533f848306ad8e4e1ebe3742026464571fd06fc884adfe3e248dddc26b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-29T11:04:01Z","title_canon_sha256":"28a29f9e9e7087b513459acf99902fca661f318cae765311f94aa8eb704781c5"},"schema_version":"1.0","source":{"id":"2607.26763","kind":"arxiv","version":1}},"canonical_sha256":"9dfd708b0b4aeefb31a8055073edafa2049b09262adf89766b40ea51145b939d","receipt":{"builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9dfd708b0b4aeefb31a8055073edafa2049b09262adf89766b40ea51145b939d","first_computed_at":"2026-07-30T01:22:13.483857Z","kind":"pith_receipt","last_reissued_at":"2026-07-30T01:22:13.483857Z","receipt_version":"0.3","signature_status":"unsigned_v0"},"source_id":"2607.26763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:700e88030dce9948054bd9ee4a31d92ff9e399054a03a1749572a3d068ca7e33","sha256:ac7a2eeb3198c004fcc2d0c59d5af65dc4fbb05c49903c133998ff925503760b"],"state_sha256":"417b69480e7959ab0908c3e2326686d44c8ecebd0e25dcffc0ff9895fc131f85"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nXkbPevAlwrrRO+dLWKEjOgUwGs9r/K4bSqhDxh/Sg4ICDo7cr4wXaxB7Xvjq0JilbAQHVa5Dd1KkRNhwuEZAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T16:32:42.364919Z","bundle_sha256":"f61dbcb309d73757db864e3ab80ae39832f3475053493d05d75003abff04c9fb"}}