{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KQS6QJQJO6RPYXGDXBWKFLJEG2","short_pith_number":"pith:KQS6QJQJ","canonical_record":{"source":{"id":"2503.02410","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-03-04T08:51:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"27c34d338162b99ff1d7419ed006467e9ee96add5d0751f8ffc7a442ef11cb6e","abstract_canon_sha256":"4b51754aa4647fdc6c7a1cd1ded4267915461a3d520e4514b6e4827d6849cae8"},"schema_version":"1.0"},"canonical_sha256":"5425e8260977a2fc5cc3b86ca2ad243699c8926678cded8702fcfc8ede8d005b","source":{"kind":"arxiv","id":"2503.02410","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.02410","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"arxiv_version","alias_value":"2503.02410v2","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02410","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"pith_short_12","alias_value":"KQS6QJQJO6RP","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"KQS6QJQJO6RPYXGD","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"KQS6QJQJ","created_at":"2026-07-05T11:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KQS6QJQJO6RPYXGDXBWKFLJEG2","target":"record","payload":{"canonical_record":{"source":{"id":"2503.02410","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-03-04T08:51:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"27c34d338162b99ff1d7419ed006467e9ee96add5d0751f8ffc7a442ef11cb6e","abstract_canon_sha256":"4b51754aa4647fdc6c7a1cd1ded4267915461a3d520e4514b6e4827d6849cae8"},"schema_version":"1.0"},"canonical_sha256":"5425e8260977a2fc5cc3b86ca2ad243699c8926678cded8702fcfc8ede8d005b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:31:43.899962Z","signature_b64":"Ss4XMlG4HgL7AU4yyjivItUcDmKVrJSHeyAt37DGjUllCirO5yVhwQ3FVXmTAm0HreDwvG3LVClWcekuWyX6CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5425e8260977a2fc5cc3b86ca2ad243699c8926678cded8702fcfc8ede8d005b","last_reissued_at":"2026-07-05T11:31:43.899423Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:31:43.899423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.02410","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":"2026-07-05T11:31:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BKLt9P1TcQk1RDpFNtz3i4NI86+tDd7j+ltFaZ+mnVeq4SRR7EQ+1u47d70VkwFw5OeVwvMD1aqlMqdgFRf/Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T23:50:23.596088Z"},"content_sha256":"f6600b8c4283409656fd214aac84d772fd0e554af1ce1a816ed7d7ed65161177","schema_version":"1.0","event_id":"sha256:f6600b8c4283409656fd214aac84d772fd0e554af1ce1a816ed7d7ed65161177"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KQS6QJQJO6RPYXGDXBWKFLJEG2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chenfei Ye, Hanyang Peng, Jiesi Hu, Pengcheng Shi, Ting Ma, Xutao Guo, Yang Shang, Yanwu Yang","submitted_at":"2025-03-04T08:51:44Z","abstract_excerpt":"In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance from context, making it particularly effective for the intricate demands of neuroimaging. However, current ICL models, limited to 2D inputs and thus exhibiting suboptimal performance, struggle to extend to 3D inputs due to the high memory demands of ICL. In this regard, we introduce Neuroverse3D, an ICL model capable of performing multiple neuroimaging tasks in 3D (e.g., segmentation, denoising, inpainting). Neurovers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02410","kind":"arxiv","version":2},"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/2503.02410/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":"2026-07-05T11:31:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"819BfBOhlumpn6d2vrio4S/dzbKzhrK6R7t5jaw1tHTzuoQruSd7QOZ46y/Bol0rQa/MEPizvmOiCTKUbTRdDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T23:50:23.649335Z"},"content_sha256":"161a69129003a730a46c50928738b3a0ec5d45c312189212124ab880219d7292","schema_version":"1.0","event_id":"sha256:161a69129003a730a46c50928738b3a0ec5d45c312189212124ab880219d7292"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KQS6QJQJO6RPYXGDXBWKFLJEG2/bundle.json","state_url":"https://pith.science/pith/KQS6QJQJO6RPYXGDXBWKFLJEG2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KQS6QJQJO6RPYXGDXBWKFLJEG2/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":"2026-08-17T23:50:23Z","links":{"resolver":"https://pith.science/pith/KQS6QJQJO6RPYXGDXBWKFLJEG2","bundle":"https://pith.science/pith/KQS6QJQJO6RPYXGDXBWKFLJEG2/bundle.json","state":"https://pith.science/pith/KQS6QJQJO6RPYXGDXBWKFLJEG2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KQS6QJQJO6RPYXGDXBWKFLJEG2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KQS6QJQJO6RPYXGDXBWKFLJEG2","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":"4b51754aa4647fdc6c7a1cd1ded4267915461a3d520e4514b6e4827d6849cae8","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-03-04T08:51:44Z","title_canon_sha256":"27c34d338162b99ff1d7419ed006467e9ee96add5d0751f8ffc7a442ef11cb6e"},"schema_version":"1.0","source":{"id":"2503.02410","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.02410","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"arxiv_version","alias_value":"2503.02410v2","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02410","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"pith_short_12","alias_value":"KQS6QJQJO6RP","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"KQS6QJQJO6RPYXGD","created_at":"2026-07-05T11:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"KQS6QJQJ","created_at":"2026-07-05T11:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:161a69129003a730a46c50928738b3a0ec5d45c312189212124ab880219d7292","target":"graph","created_at":"2026-07-05T11:31:43Z","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/2503.02410/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance from context, making it particularly effective for the intricate demands of neuroimaging. However, current ICL models, limited to 2D inputs and thus exhibiting suboptimal performance, struggle to extend to 3D inputs due to the high memory demands of ICL. In this regard, we introduce Neuroverse3D, an ICL model capable of performing multiple neuroimaging tasks in 3D (e.g., segmentation, denoising, inpainting). Neurovers","authors_text":"Chenfei Ye, Hanyang Peng, Jiesi Hu, Pengcheng Shi, Ting Ma, Xutao Guo, Yang Shang, Yanwu Yang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-03-04T08:51:44Z","title":"Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02410","kind":"arxiv","version":2},"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:f6600b8c4283409656fd214aac84d772fd0e554af1ce1a816ed7d7ed65161177","target":"record","created_at":"2026-07-05T11:31:43Z","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":"4b51754aa4647fdc6c7a1cd1ded4267915461a3d520e4514b6e4827d6849cae8","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-03-04T08:51:44Z","title_canon_sha256":"27c34d338162b99ff1d7419ed006467e9ee96add5d0751f8ffc7a442ef11cb6e"},"schema_version":"1.0","source":{"id":"2503.02410","kind":"arxiv","version":2}},"canonical_sha256":"5425e8260977a2fc5cc3b86ca2ad243699c8926678cded8702fcfc8ede8d005b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5425e8260977a2fc5cc3b86ca2ad243699c8926678cded8702fcfc8ede8d005b","first_computed_at":"2026-07-05T11:31:43.899423Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:31:43.899423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ss4XMlG4HgL7AU4yyjivItUcDmKVrJSHeyAt37DGjUllCirO5yVhwQ3FVXmTAm0HreDwvG3LVClWcekuWyX6CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:31:43.899962Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.02410","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f6600b8c4283409656fd214aac84d772fd0e554af1ce1a816ed7d7ed65161177","sha256:161a69129003a730a46c50928738b3a0ec5d45c312189212124ab880219d7292"],"state_sha256":"cedf103eed3d3815c733c97f1e2564146ed3e13d22ff6ce83124fa14e9f7f4cb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KPNhVCrgSHIEqfSNztPo6BKlMKy+roNHZjjl54ZFN8MhLBm1TmidOCJm9PNhB1TOk/uTNdiSrijCM4hJVqQrBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T23:50:23.668329Z","bundle_sha256":"c4a43194192ba8be41325a0291b2b58da117d271bfeb98e13c0811bd0dd68eb9"}}