{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4NPQIZEIJ7BZCICTXSQYH6MAOS","short_pith_number":"pith:4NPQIZEI","canonical_record":{"source":{"id":"2606.11870","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-10T09:47:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6c292e33b8f806e7eef4cdf38836c46671fccd93cf2a603e5f4c4ea2ee201083","abstract_canon_sha256":"f590d127ba3abd70d9f3c775b84d6a3145adb53ce42663c949be37c6d03480b3"},"schema_version":"1.0"},"canonical_sha256":"e35f0464884fc3912053bca183f9807487ef42a188a4445fdadfe41ba594e9dc","source":{"kind":"arxiv","id":"2606.11870","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11870","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11870v1","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11870","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_12","alias_value":"4NPQIZEIJ7BZ","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_16","alias_value":"4NPQIZEIJ7BZCICT","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_8","alias_value":"4NPQIZEI","created_at":"2026-06-11T01:10:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4NPQIZEIJ7BZCICTXSQYH6MAOS","target":"record","payload":{"canonical_record":{"source":{"id":"2606.11870","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-10T09:47:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6c292e33b8f806e7eef4cdf38836c46671fccd93cf2a603e5f4c4ea2ee201083","abstract_canon_sha256":"f590d127ba3abd70d9f3c775b84d6a3145adb53ce42663c949be37c6d03480b3"},"schema_version":"1.0"},"canonical_sha256":"e35f0464884fc3912053bca183f9807487ef42a188a4445fdadfe41ba594e9dc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:10:12.911641Z","signature_b64":"v7r45KnYn2uQutOx5VqBIUfzblj+yTvT9b1NO3OB8G8ecgLNhyTc6vJN1sIMNNl7YbwPaDpuicrUPptK1q4GCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e35f0464884fc3912053bca183f9807487ef42a188a4445fdadfe41ba594e9dc","last_reissued_at":"2026-06-11T01:10:12.910792Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:10:12.910792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.11870","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":"2026-06-11T01:10:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c8v5KsKY29buscbSggxPbSbB4qlPsLqFmPNYDIIn8PIaF6FCqfDjxVtySDdK9Q+62p5ORIbJUGa3dBuiln+PCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T06:36:35.526527Z"},"content_sha256":"ad9e7302e7da97b5e095c1731a9380862aab4c82fd828e0906864035e7f167e2","schema_version":"1.0","event_id":"sha256:ad9e7302e7da97b5e095c1731a9380862aab4c82fd828e0906864035e7f167e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4NPQIZEIJ7BZCICTXSQYH6MAOS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modelling magnetic material properties with uncertainty-aware neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Akihito Kinoshita, Akira Kato, Alexander Kovacs, Clemens Wager, Harald Oezelt, Hayate Yamano, Heisam Moustafa, Hyuga Hosoi, Masao Yano, Noritsugu Sakuma, Qais Ali, Tetsuya Shoji, Thomas Schrefl","submitted_at":"2026-06-10T09:47:21Z","abstract_excerpt":"Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces. Yet, the scarcity of high-quality data and the frequent need for out-of-distribution prediction introduce substantial uncertainty, making the assessment of model reliability essential. In this work, we investigate uncertainty quantification as a means to evaluate model confidence in the context of permanent magnet research. In a first study, we benchmark classical and modern machine learning models for predicting intrinsic magnetic properties, f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11870","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/2606.11870/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":"2026-06-11T01:10:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CkCD0ao2YJBnBKC7RUeX/kkGTuMrW2z9PPkvgr2jtml04QDnfEjEAvdzVakjpEkUhKlcm3VIUV17aIsTpTMqCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T06:36:35.526906Z"},"content_sha256":"75fe89d32a15cda09685b32e20f194da11955a4fac85159fdf982945e37135b0","schema_version":"1.0","event_id":"sha256:75fe89d32a15cda09685b32e20f194da11955a4fac85159fdf982945e37135b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4NPQIZEIJ7BZCICTXSQYH6MAOS/bundle.json","state_url":"https://pith.science/pith/4NPQIZEIJ7BZCICTXSQYH6MAOS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4NPQIZEIJ7BZCICTXSQYH6MAOS/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":"2026-06-29T06:36:35Z","links":{"resolver":"https://pith.science/pith/4NPQIZEIJ7BZCICTXSQYH6MAOS","bundle":"https://pith.science/pith/4NPQIZEIJ7BZCICTXSQYH6MAOS/bundle.json","state":"https://pith.science/pith/4NPQIZEIJ7BZCICTXSQYH6MAOS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4NPQIZEIJ7BZCICTXSQYH6MAOS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4NPQIZEIJ7BZCICTXSQYH6MAOS","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":"f590d127ba3abd70d9f3c775b84d6a3145adb53ce42663c949be37c6d03480b3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-10T09:47:21Z","title_canon_sha256":"6c292e33b8f806e7eef4cdf38836c46671fccd93cf2a603e5f4c4ea2ee201083"},"schema_version":"1.0","source":{"id":"2606.11870","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11870","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11870v1","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11870","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_12","alias_value":"4NPQIZEIJ7BZ","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_16","alias_value":"4NPQIZEIJ7BZCICT","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_8","alias_value":"4NPQIZEI","created_at":"2026-06-11T01:10:12Z"}],"graph_snapshots":[{"event_id":"sha256:75fe89d32a15cda09685b32e20f194da11955a4fac85159fdf982945e37135b0","target":"graph","created_at":"2026-06-11T01:10:12Z","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/2606.11870/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces. Yet, the scarcity of high-quality data and the frequent need for out-of-distribution prediction introduce substantial uncertainty, making the assessment of model reliability essential. In this work, we investigate uncertainty quantification as a means to evaluate model confidence in the context of permanent magnet research. In a first study, we benchmark classical and modern machine learning models for predicting intrinsic magnetic properties, f","authors_text":"Akihito Kinoshita, Akira Kato, Alexander Kovacs, Clemens Wager, Harald Oezelt, Hayate Yamano, Heisam Moustafa, Hyuga Hosoi, Masao Yano, Noritsugu Sakuma, Qais Ali, Tetsuya Shoji, Thomas Schrefl","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-10T09:47:21Z","title":"Modelling magnetic material properties with uncertainty-aware neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11870","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:ad9e7302e7da97b5e095c1731a9380862aab4c82fd828e0906864035e7f167e2","target":"record","created_at":"2026-06-11T01:10:12Z","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":"f590d127ba3abd70d9f3c775b84d6a3145adb53ce42663c949be37c6d03480b3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-10T09:47:21Z","title_canon_sha256":"6c292e33b8f806e7eef4cdf38836c46671fccd93cf2a603e5f4c4ea2ee201083"},"schema_version":"1.0","source":{"id":"2606.11870","kind":"arxiv","version":1}},"canonical_sha256":"e35f0464884fc3912053bca183f9807487ef42a188a4445fdadfe41ba594e9dc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e35f0464884fc3912053bca183f9807487ef42a188a4445fdadfe41ba594e9dc","first_computed_at":"2026-06-11T01:10:12.910792Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:10:12.910792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v7r45KnYn2uQutOx5VqBIUfzblj+yTvT9b1NO3OB8G8ecgLNhyTc6vJN1sIMNNl7YbwPaDpuicrUPptK1q4GCA==","signature_status":"signed_v1","signed_at":"2026-06-11T01:10:12.911641Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.11870","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad9e7302e7da97b5e095c1731a9380862aab4c82fd828e0906864035e7f167e2","sha256:75fe89d32a15cda09685b32e20f194da11955a4fac85159fdf982945e37135b0"],"state_sha256":"8e210a731636d0849015f43851b4c534e098c1434b6a6f72ac9492beaadcf157"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v30+/023mZUrVKtzlAJ+mKWZOb3R/SZR737L1TTLSmRZ9ctZoqaykyyXzY5zdf84p5ttYaHQZIVZN/DidTSQAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T06:36:35.528835Z","bundle_sha256":"4d1dbc1c3cfdbf95bae1567bbd2436fd80edbfd876b51f87eb91d933ca6c4a52"}}