{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:57GSLNYYVQMPGLWSTNHQ2S4KJZ","short_pith_number":"pith:57GSLNYY","canonical_record":{"source":{"id":"2606.07196","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T12:04:08Z","cross_cats_sorted":[],"title_canon_sha256":"528f3da686c38289f87b00ae69df1b6f432cc53394acccc3d38c7a86f9aa8972","abstract_canon_sha256":"66aac19a9d6aedbb43a136f5c94ec75787e2b32e4bcbdb2b46ad73c4243f5fe6"},"schema_version":"1.0"},"canonical_sha256":"efcd25b718ac18f32ed29b4f0d4b8a4e40f686f835c24303abc71eeb085c53eb","source":{"kind":"arxiv","id":"2606.07196","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07196","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07196v1","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07196","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"pith_short_12","alias_value":"57GSLNYYVQMP","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"pith_short_16","alias_value":"57GSLNYYVQMPGLWS","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"pith_short_8","alias_value":"57GSLNYY","created_at":"2026-06-08T01:04:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:57GSLNYYVQMPGLWSTNHQ2S4KJZ","target":"record","payload":{"canonical_record":{"source":{"id":"2606.07196","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T12:04:08Z","cross_cats_sorted":[],"title_canon_sha256":"528f3da686c38289f87b00ae69df1b6f432cc53394acccc3d38c7a86f9aa8972","abstract_canon_sha256":"66aac19a9d6aedbb43a136f5c94ec75787e2b32e4bcbdb2b46ad73c4243f5fe6"},"schema_version":"1.0"},"canonical_sha256":"efcd25b718ac18f32ed29b4f0d4b8a4e40f686f835c24303abc71eeb085c53eb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:51.959892Z","signature_b64":"R4JfMe/tH/1GpSjXYyvbfPdZCcNC1y10zm4g1liMyptjWhoFu/KRQVPh8LD3smekQjmCTOXnzg7lPD/9XJjkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efcd25b718ac18f32ed29b4f0d4b8a4e40f686f835c24303abc71eeb085c53eb","last_reissued_at":"2026-06-08T01:04:51.959079Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:51.959079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.07196","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":"2026-06-08T01:04:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5FDcBFHCnPX04n7h7puxGBGiNVQXIW6nciDF9Q2pVGvnWuKs/2MLSpjJAbQRepiXPW/D6FsyOE6Rk9yXs57pBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T09:34:30.806391Z"},"content_sha256":"4107685dcbb1912c7e1192f926732d5f56c68efde58240cbf6d6e5b8e03c3031","schema_version":"1.0","event_id":"sha256:4107685dcbb1912c7e1192f926732d5f56c68efde58240cbf6d6e5b8e03c3031"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:57GSLNYYVQMPGLWSTNHQ2S4KJZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ismail Huseynov, Marco Morik, Shinichi Nakajima, Stefan Haufe, Xiao Ruiting","submitted_at":"2026-06-05T12:04:08Z","abstract_excerpt":"Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterative update rules. Although these updates are principled and interpretable, their dynamics cannot be adapted from data. We propose to learn the update mechanism itself while preserving the underlying Bayesian structure by unfolding a classical joint hyperparameter-learning solver into a trainable neural architecture whose layers mirror the original iterations. The resulting framework is initialized to recover the classical solver exactly before "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07196","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/2606.07196/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":"2026-06-08T01:04:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fiI+vYguF4sPB8jSg5LAFjgNtAyCsiNy2KpYYFH5Lic8aWQkB/+TXjXWfGyWN7wNxSi+AY93pKsfgO31xIhsAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T09:34:30.806780Z"},"content_sha256":"6794c16eb1fffa7fe63a93ab1ff0636b673f8730e6c6b8ee701d4bfeef39013a","schema_version":"1.0","event_id":"sha256:6794c16eb1fffa7fe63a93ab1ff0636b673f8730e6c6b8ee701d4bfeef39013a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/57GSLNYYVQMPGLWSTNHQ2S4KJZ/bundle.json","state_url":"https://pith.science/pith/57GSLNYYVQMPGLWSTNHQ2S4KJZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/57GSLNYYVQMPGLWSTNHQ2S4KJZ/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":"2026-06-12T09:34:30Z","links":{"resolver":"https://pith.science/pith/57GSLNYYVQMPGLWSTNHQ2S4KJZ","bundle":"https://pith.science/pith/57GSLNYYVQMPGLWSTNHQ2S4KJZ/bundle.json","state":"https://pith.science/pith/57GSLNYYVQMPGLWSTNHQ2S4KJZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/57GSLNYYVQMPGLWSTNHQ2S4KJZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:57GSLNYYVQMPGLWSTNHQ2S4KJZ","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":"66aac19a9d6aedbb43a136f5c94ec75787e2b32e4bcbdb2b46ad73c4243f5fe6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T12:04:08Z","title_canon_sha256":"528f3da686c38289f87b00ae69df1b6f432cc53394acccc3d38c7a86f9aa8972"},"schema_version":"1.0","source":{"id":"2606.07196","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07196","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07196v1","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07196","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"pith_short_12","alias_value":"57GSLNYYVQMP","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"pith_short_16","alias_value":"57GSLNYYVQMPGLWS","created_at":"2026-06-08T01:04:51Z"},{"alias_kind":"pith_short_8","alias_value":"57GSLNYY","created_at":"2026-06-08T01:04:51Z"}],"graph_snapshots":[{"event_id":"sha256:6794c16eb1fffa7fe63a93ab1ff0636b673f8730e6c6b8ee701d4bfeef39013a","target":"graph","created_at":"2026-06-08T01:04:51Z","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/2606.07196/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterative update rules. Although these updates are principled and interpretable, their dynamics cannot be adapted from data. We propose to learn the update mechanism itself while preserving the underlying Bayesian structure by unfolding a classical joint hyperparameter-learning solver into a trainable neural architecture whose layers mirror the original iterations. The resulting framework is initialized to recover the classical solver exactly before ","authors_text":"Ismail Huseynov, Marco Morik, Shinichi Nakajima, Stefan Haufe, Xiao Ruiting","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T12:04:08Z","title":"Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07196","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":"sha256:4107685dcbb1912c7e1192f926732d5f56c68efde58240cbf6d6e5b8e03c3031","target":"record","created_at":"2026-06-08T01:04:51Z","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":"66aac19a9d6aedbb43a136f5c94ec75787e2b32e4bcbdb2b46ad73c4243f5fe6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T12:04:08Z","title_canon_sha256":"528f3da686c38289f87b00ae69df1b6f432cc53394acccc3d38c7a86f9aa8972"},"schema_version":"1.0","source":{"id":"2606.07196","kind":"arxiv","version":1}},"canonical_sha256":"efcd25b718ac18f32ed29b4f0d4b8a4e40f686f835c24303abc71eeb085c53eb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efcd25b718ac18f32ed29b4f0d4b8a4e40f686f835c24303abc71eeb085c53eb","first_computed_at":"2026-06-08T01:04:51.959079Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:04:51.959079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R4JfMe/tH/1GpSjXYyvbfPdZCcNC1y10zm4g1liMyptjWhoFu/KRQVPh8LD3smekQjmCTOXnzg7lPD/9XJjkCQ==","signature_status":"signed_v1","signed_at":"2026-06-08T01:04:51.959892Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07196","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4107685dcbb1912c7e1192f926732d5f56c68efde58240cbf6d6e5b8e03c3031","sha256:6794c16eb1fffa7fe63a93ab1ff0636b673f8730e6c6b8ee701d4bfeef39013a"],"state_sha256":"3b21ff8342e46e4bff7695c68071f2487a9dd2484667ffa36dfcc05a2bb80adb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xTZS+hxhfHnSvS9OhwdpCL3qUGuyow09SKs4Q84YO8B8alOXUO29XP/fnJqYob678TYIl/cgNC3KDTzObmgVAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T09:34:30.808835Z","bundle_sha256":"cc4b6791dc678863dbf404d3875f5afff2dd7bd45bf7950f0fabdb3a280fa4ef"}}