{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YTUT36KBPZ32WKIXH2H2R4GURE","short_pith_number":"pith:YTUT36KB","canonical_record":{"source":{"id":"2506.23782","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-30T12:23:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b09a29ff44cc429abbb431f646eb578a800290090d3bf4099c854c8fc52a98cb","abstract_canon_sha256":"8fe441e9ed74362e3dcc2d0fac73c0bf65d2ebec59eee1c62b6421a119e859b5"},"schema_version":"1.0"},"canonical_sha256":"c4e93df9417e77ab29173e8fa8f0d4891d4c7220c32c0315254f010021c7297f","source":{"kind":"arxiv","id":"2506.23782","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.23782","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"arxiv_version","alias_value":"2506.23782v2","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23782","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"pith_short_12","alias_value":"YTUT36KBPZ32","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"pith_short_16","alias_value":"YTUT36KBPZ32WKIX","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"pith_short_8","alias_value":"YTUT36KB","created_at":"2026-07-05T11:33:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YTUT36KBPZ32WKIXH2H2R4GURE","target":"record","payload":{"canonical_record":{"source":{"id":"2506.23782","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-30T12:23:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b09a29ff44cc429abbb431f646eb578a800290090d3bf4099c854c8fc52a98cb","abstract_canon_sha256":"8fe441e9ed74362e3dcc2d0fac73c0bf65d2ebec59eee1c62b6421a119e859b5"},"schema_version":"1.0"},"canonical_sha256":"c4e93df9417e77ab29173e8fa8f0d4891d4c7220c32c0315254f010021c7297f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:25.612000Z","signature_b64":"wyUswoVBpVS4xfScwMlBt7y/wnmbYaFqhjGfjb7ZYvsZwl1ujqjbmL4JvBd7k7zqlD7i0OZuTySvKnaHT8AFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4e93df9417e77ab29173e8fa8f0d4891d4c7220c32c0315254f010021c7297f","last_reissued_at":"2026-07-05T11:33:25.611357Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:25.611357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.23782","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":"2026-07-05T11:33:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H9AcaPnMwgukpKRpdsWeZvTvx24vSp9ZTngBHpWIxyTr23vvwBeL6C9il7Y9Lfzk3aGrY0zFKwSaiuuYIYfNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:42:01.761012Z"},"content_sha256":"a6dcac60b1739fa1f731706c5c338c1f65d9c83823ed789fe81f30ccb8d0ca7f","schema_version":"1.0","event_id":"sha256:a6dcac60b1739fa1f731706c5c338c1f65d9c83823ed789fe81f30ccb8d0ca7f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YTUT36KBPZ32WKIXH2H2R4GURE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"WATS: Calibrating Graph Neural Networks with Wavelet-Aware Temperature Scaling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chang Xu, Haohui Lu, Junbin Gao, Linwei Tao, Minjing Dong, Xiaoyang Li","submitted_at":"2025-06-30T12:23:57Z","abstract_excerpt":"Graph Neural Networks (GNNs) have demonstrated strong predictive performance on relational data; however, their confidence estimates often misalign with actual predictive correctness, posing significant limitations for deployment in safety-critical settings. While existing graph-aware calibration methods seek to mitigate this limitation, they primarily depend on coarse one-hop statistics, such as neighbor-predicted confidence, or latent node embeddings, thereby neglecting the fine-grained structural heterogeneity inherent in graph topology. In this work, we propose Wavelet-Aware Temperature Sc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23782","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/2506.23782/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":"2026-07-05T11:33:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OI33p/jRzpqD2m+hrZCQUOzBnHOlQ3qiR9k2UAM3Af8CzCi6xS8JGW2uJW2iRwfl/Wb/aym4JbXNa10tQFVKBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:42:01.761302Z"},"content_sha256":"608f7df0c5c9c7d14b099c7de3cbd29c0150fe67262f13f87193ec3b4686240c","schema_version":"1.0","event_id":"sha256:608f7df0c5c9c7d14b099c7de3cbd29c0150fe67262f13f87193ec3b4686240c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YTUT36KBPZ32WKIXH2H2R4GURE/bundle.json","state_url":"https://pith.science/pith/YTUT36KBPZ32WKIXH2H2R4GURE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YTUT36KBPZ32WKIXH2H2R4GURE/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":"2026-08-05T17:42:01Z","links":{"resolver":"https://pith.science/pith/YTUT36KBPZ32WKIXH2H2R4GURE","bundle":"https://pith.science/pith/YTUT36KBPZ32WKIXH2H2R4GURE/bundle.json","state":"https://pith.science/pith/YTUT36KBPZ32WKIXH2H2R4GURE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YTUT36KBPZ32WKIXH2H2R4GURE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YTUT36KBPZ32WKIXH2H2R4GURE","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":"8fe441e9ed74362e3dcc2d0fac73c0bf65d2ebec59eee1c62b6421a119e859b5","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-30T12:23:57Z","title_canon_sha256":"b09a29ff44cc429abbb431f646eb578a800290090d3bf4099c854c8fc52a98cb"},"schema_version":"1.0","source":{"id":"2506.23782","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.23782","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"arxiv_version","alias_value":"2506.23782v2","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23782","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"pith_short_12","alias_value":"YTUT36KBPZ32","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"pith_short_16","alias_value":"YTUT36KBPZ32WKIX","created_at":"2026-07-05T11:33:25Z"},{"alias_kind":"pith_short_8","alias_value":"YTUT36KB","created_at":"2026-07-05T11:33:25Z"}],"graph_snapshots":[{"event_id":"sha256:608f7df0c5c9c7d14b099c7de3cbd29c0150fe67262f13f87193ec3b4686240c","target":"graph","created_at":"2026-07-05T11:33:25Z","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/2506.23782/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have demonstrated strong predictive performance on relational data; however, their confidence estimates often misalign with actual predictive correctness, posing significant limitations for deployment in safety-critical settings. While existing graph-aware calibration methods seek to mitigate this limitation, they primarily depend on coarse one-hop statistics, such as neighbor-predicted confidence, or latent node embeddings, thereby neglecting the fine-grained structural heterogeneity inherent in graph topology. In this work, we propose Wavelet-Aware Temperature Sc","authors_text":"Chang Xu, Haohui Lu, Junbin Gao, Linwei Tao, Minjing Dong, Xiaoyang Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-30T12:23:57Z","title":"WATS: Calibrating Graph Neural Networks with Wavelet-Aware Temperature Scaling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23782","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:a6dcac60b1739fa1f731706c5c338c1f65d9c83823ed789fe81f30ccb8d0ca7f","target":"record","created_at":"2026-07-05T11:33:25Z","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":"8fe441e9ed74362e3dcc2d0fac73c0bf65d2ebec59eee1c62b6421a119e859b5","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-30T12:23:57Z","title_canon_sha256":"b09a29ff44cc429abbb431f646eb578a800290090d3bf4099c854c8fc52a98cb"},"schema_version":"1.0","source":{"id":"2506.23782","kind":"arxiv","version":2}},"canonical_sha256":"c4e93df9417e77ab29173e8fa8f0d4891d4c7220c32c0315254f010021c7297f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c4e93df9417e77ab29173e8fa8f0d4891d4c7220c32c0315254f010021c7297f","first_computed_at":"2026-07-05T11:33:25.611357Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:25.611357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wyUswoVBpVS4xfScwMlBt7y/wnmbYaFqhjGfjb7ZYvsZwl1ujqjbmL4JvBd7k7zqlD7i0OZuTySvKnaHT8AFAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:25.612000Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.23782","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a6dcac60b1739fa1f731706c5c338c1f65d9c83823ed789fe81f30ccb8d0ca7f","sha256:608f7df0c5c9c7d14b099c7de3cbd29c0150fe67262f13f87193ec3b4686240c"],"state_sha256":"03ee813b852b4e5315a68ee1776c4e35f0f17aaed1de7ef316c6d43e2a75fed3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"URX6Xf1tsRqzeVi8nsJker7pdX4f9w/tFAvcJ9JJyECPBm5maitDtvAJNLniowcVyNWgPpZ7W+NGScZ382mBBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T17:42:01.764180Z","bundle_sha256":"38758526c0470db64dc1f13fd8e6c0ba27a367107c6f8bd8bb3f8270f39e2553"}}