{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:UOFHWX5HROOSOLNDB63D3TC3DH","short_pith_number":"pith:UOFHWX5H","canonical_record":{"source":{"id":"2606.11500","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-09T22:45:45Z","cross_cats_sorted":["cs.CE","cs.IT","cs.LG","math.IT","q-bio.NC"],"title_canon_sha256":"d8b4eed3416e2fae10294dc164a062e76c31e8f3a09c15cba477b81e2d390fea","abstract_canon_sha256":"cc9dadba165a8c84b2d0498ea668e3bdf85c4bce699220bfdb8c037ed0f641a9"},"schema_version":"1.0"},"canonical_sha256":"a38a7b5fa78b9d272da30fb63dcc5b19f3f74d39b3df80662e4bc94998c63d08","source":{"kind":"arxiv","id":"2606.11500","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11500","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11500v1","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11500","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"UOFHWX5HROOS","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"UOFHWX5HROOSOLND","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"UOFHWX5H","created_at":"2026-06-11T01:09:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:UOFHWX5HROOSOLNDB63D3TC3DH","target":"record","payload":{"canonical_record":{"source":{"id":"2606.11500","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-09T22:45:45Z","cross_cats_sorted":["cs.CE","cs.IT","cs.LG","math.IT","q-bio.NC"],"title_canon_sha256":"d8b4eed3416e2fae10294dc164a062e76c31e8f3a09c15cba477b81e2d390fea","abstract_canon_sha256":"cc9dadba165a8c84b2d0498ea668e3bdf85c4bce699220bfdb8c037ed0f641a9"},"schema_version":"1.0"},"canonical_sha256":"a38a7b5fa78b9d272da30fb63dcc5b19f3f74d39b3df80662e4bc94998c63d08","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:52.599592Z","signature_b64":"c9MqWPqYko743MNP5507OotMRDKAIlNAY4pMH/wADQD+w4rLDk5k+CSDqQlcsmWHf5Y045UVFFN4w8Vz3whjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a38a7b5fa78b9d272da30fb63dcc5b19f3f74d39b3df80662e4bc94998c63d08","last_reissued_at":"2026-06-11T01:09:52.598925Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:52.598925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.11500","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":"2026-06-11T01:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TnE2OVz19qWCCgs1qS59sd6HenzfQtYR8nPF5VxuXFSuZDl6LGaGA/aIvuTWkKSPtkD4lw8w1V4tXZaFq/PHAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T03:24:58.018705Z"},"content_sha256":"00821f50e273b1121e28183c7183438f5581af98c01e9669da7a028f943d5e13","schema_version":"1.0","event_id":"sha256:00821f50e273b1121e28183c7183438f5581af98c01e9669da7a028f943d5e13"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:UOFHWX5HROOSOLNDB63D3TC3DH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CE","cs.IT","cs.LG","math.IT","q-bio.NC"],"primary_cat":"eess.IV","authors_text":"Hongkai Wen, Junfeng Xia, Minghao Xu, Mo Wang, Quanying Liu, Wenhao Ye","submitted_at":"2026-06-09T22:45:45Z","abstract_excerpt":"The success of large-scale deep learning models in neuroscience is fundamentally constrained by severe data heterogeneity. Native fMRI data aggregated from diverse sources exhibit substantial variation in both spatial and temporal resolutions. Consequently, most existing frameworks rely on lengthy, rigid preprocessing pipelines that enforce uniformity across datasets. This practice introduces two critical limitations: (1) potential degradation of subject-specific anatomical information; (2) significant computational overhead, often requiring hours of processing per subject. Here, we propose Fl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11500","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/2606.11500/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":"2026-06-11T01:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KxOhBBcdvH7Qby4TUv+rVb8tqQMp3MfiOxu/OUhHjd+xUPQO0hH936fCAWRIw6P148N8ofAk0yBSxeUi5SVmBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T03:24:58.019073Z"},"content_sha256":"92752a110671c46bc77ed078b8e8ee190e2f5188e961ca924873ef1ca7aadd8a","schema_version":"1.0","event_id":"sha256:92752a110671c46bc77ed078b8e8ee190e2f5188e961ca924873ef1ca7aadd8a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UOFHWX5HROOSOLNDB63D3TC3DH/bundle.json","state_url":"https://pith.science/pith/UOFHWX5HROOSOLNDB63D3TC3DH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UOFHWX5HROOSOLNDB63D3TC3DH/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":"2026-06-28T03:24:58Z","links":{"resolver":"https://pith.science/pith/UOFHWX5HROOSOLNDB63D3TC3DH","bundle":"https://pith.science/pith/UOFHWX5HROOSOLNDB63D3TC3DH/bundle.json","state":"https://pith.science/pith/UOFHWX5HROOSOLNDB63D3TC3DH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UOFHWX5HROOSOLNDB63D3TC3DH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UOFHWX5HROOSOLNDB63D3TC3DH","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":"cc9dadba165a8c84b2d0498ea668e3bdf85c4bce699220bfdb8c037ed0f641a9","cross_cats_sorted":["cs.CE","cs.IT","cs.LG","math.IT","q-bio.NC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-09T22:45:45Z","title_canon_sha256":"d8b4eed3416e2fae10294dc164a062e76c31e8f3a09c15cba477b81e2d390fea"},"schema_version":"1.0","source":{"id":"2606.11500","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11500","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11500v1","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11500","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"UOFHWX5HROOS","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"UOFHWX5HROOSOLND","created_at":"2026-06-11T01:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"UOFHWX5H","created_at":"2026-06-11T01:09:52Z"}],"graph_snapshots":[{"event_id":"sha256:92752a110671c46bc77ed078b8e8ee190e2f5188e961ca924873ef1ca7aadd8a","target":"graph","created_at":"2026-06-11T01:09:52Z","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/2606.11500/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The success of large-scale deep learning models in neuroscience is fundamentally constrained by severe data heterogeneity. Native fMRI data aggregated from diverse sources exhibit substantial variation in both spatial and temporal resolutions. Consequently, most existing frameworks rely on lengthy, rigid preprocessing pipelines that enforce uniformity across datasets. This practice introduces two critical limitations: (1) potential degradation of subject-specific anatomical information; (2) significant computational overhead, often requiring hours of processing per subject. Here, we propose Fl","authors_text":"Hongkai Wen, Junfeng Xia, Minghao Xu, Mo Wang, Quanying Liu, Wenhao Ye","cross_cats":["cs.CE","cs.IT","cs.LG","math.IT","q-bio.NC"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-09T22:45:45Z","title":"FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11500","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":"sha256:00821f50e273b1121e28183c7183438f5581af98c01e9669da7a028f943d5e13","target":"record","created_at":"2026-06-11T01:09:52Z","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":"cc9dadba165a8c84b2d0498ea668e3bdf85c4bce699220bfdb8c037ed0f641a9","cross_cats_sorted":["cs.CE","cs.IT","cs.LG","math.IT","q-bio.NC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-09T22:45:45Z","title_canon_sha256":"d8b4eed3416e2fae10294dc164a062e76c31e8f3a09c15cba477b81e2d390fea"},"schema_version":"1.0","source":{"id":"2606.11500","kind":"arxiv","version":1}},"canonical_sha256":"a38a7b5fa78b9d272da30fb63dcc5b19f3f74d39b3df80662e4bc94998c63d08","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a38a7b5fa78b9d272da30fb63dcc5b19f3f74d39b3df80662e4bc94998c63d08","first_computed_at":"2026-06-11T01:09:52.598925Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:09:52.598925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"c9MqWPqYko743MNP5507OotMRDKAIlNAY4pMH/wADQD+w4rLDk5k+CSDqQlcsmWHf5Y045UVFFN4w8Vz3whjAQ==","signature_status":"signed_v1","signed_at":"2026-06-11T01:09:52.599592Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.11500","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00821f50e273b1121e28183c7183438f5581af98c01e9669da7a028f943d5e13","sha256:92752a110671c46bc77ed078b8e8ee190e2f5188e961ca924873ef1ca7aadd8a"],"state_sha256":"acca6732b8d2866021deaa2796668b1f559642ad1721904e49f2f8d3a99fe5cd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YTjuB2Df1W/2IROIjzht1uZF/JUW0MjOjdcefAooc0zqI3RlQ4ZWsdpvyRZlrRzHVoGaKhP4KDSOp+jnE7pwDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T03:24:58.021303Z","bundle_sha256":"a28800ec410cb11528a4b85fe3f682b9f420ff250d401778c6a93f670e7dc16f"}}