{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:IISMFZKXHZMZ7U7WLZRJXBZU3T","short_pith_number":"pith:IISMFZKX","canonical_record":{"source":{"id":"2605.24773","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T23:22:59Z","cross_cats_sorted":[],"title_canon_sha256":"054b205077094781ce0f36b2d452c7d82de3e7d5b06e2c6c2420d1616487c79e","abstract_canon_sha256":"2d582129c6966ac32a670233e063ae2b8c5d71ed2842e566ea7546a1cd300a53"},"schema_version":"1.0"},"canonical_sha256":"4224c2e5573e599fd3f65e629b8734dcc36a2562c4810a76a757b6a64c7f3693","source":{"kind":"arxiv","id":"2605.24773","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24773","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24773v1","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24773","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"pith_short_12","alias_value":"IISMFZKXHZMZ","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"pith_short_16","alias_value":"IISMFZKXHZMZ7U7W","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"pith_short_8","alias_value":"IISMFZKX","created_at":"2026-05-26T01:03:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:IISMFZKXHZMZ7U7WLZRJXBZU3T","target":"record","payload":{"canonical_record":{"source":{"id":"2605.24773","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T23:22:59Z","cross_cats_sorted":[],"title_canon_sha256":"054b205077094781ce0f36b2d452c7d82de3e7d5b06e2c6c2420d1616487c79e","abstract_canon_sha256":"2d582129c6966ac32a670233e063ae2b8c5d71ed2842e566ea7546a1cd300a53"},"schema_version":"1.0"},"canonical_sha256":"4224c2e5573e599fd3f65e629b8734dcc36a2562c4810a76a757b6a64c7f3693","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:57.482689Z","signature_b64":"uVkCMIfmHPLjABCxc4XC1wTUeNXnZ92AwIMvbAL9cAa1LFbjPnSm1vB09GnINSq/VskLGpqj5Uyv0kouqVQjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4224c2e5573e599fd3f65e629b8734dcc36a2562c4810a76a757b6a64c7f3693","last_reissued_at":"2026-05-26T01:03:57.481802Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:57.481802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.24773","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":"2026-05-26T01:03:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vZzK5vc/8d1r7TY4RKwiepSxHVivNlnGJAwQuCKHqMG7wlpoDoSsP3D84tiE5gpiQJB5fGLxJszUk5sNbndtCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T20:09:49.671146Z"},"content_sha256":"8e600f48f45d4034419d8e7f8df1f22e64239fa1b741262629bc4d5d69c10097","schema_version":"1.0","event_id":"sha256:8e600f48f45d4034419d8e7f8df1f22e64239fa1b741262629bc4d5d69c10097"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:IISMFZKXHZMZ7U7WLZRJXBZU3T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty Decomposition via Cyclical SG-MCMC and Soft-label Learning for Subjective NLP","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Keito Inoshita, Takato Ueno","submitted_at":"2026-05-23T23:22:59Z","abstract_excerpt":"Annotator disagreement in emotion classification reflects ambiguity intrinsic to emotion concepts and is essential for predictor-quality assessment in subjective NLP. Yet no prior work integrates soft-label learning with Bayesian deep learning to evaluate uncertainty along axes including annotator-distribution fidelity. We train a linear head on a frozen RoBERTa via cyclical stochastic gradient Markov chain Monte Carlo (cSG-MCMC), targeting the empirical annotator distribution with a soft-label objective under a five-axis evaluation. On the 28-emotion GoEmotions benchmark, the proposed method "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24773","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/2605.24773/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":"2026-05-26T01:03:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l0Xy8UNDaKXpPvn9JoEbIFJxKqaaxNgLBVhILz9xGbhl3iphZapejt+JIx712eEPy1rXNlK1RQ/mwBpigNL/CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T20:09:49.671938Z"},"content_sha256":"310e117c7c5d94b8606dcae528cb76daf70a445fe445878e2d25a62ba9d73fa3","schema_version":"1.0","event_id":"sha256:310e117c7c5d94b8606dcae528cb76daf70a445fe445878e2d25a62ba9d73fa3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IISMFZKXHZMZ7U7WLZRJXBZU3T/bundle.json","state_url":"https://pith.science/pith/IISMFZKXHZMZ7U7WLZRJXBZU3T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IISMFZKXHZMZ7U7WLZRJXBZU3T/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":"2026-06-11T20:09:49Z","links":{"resolver":"https://pith.science/pith/IISMFZKXHZMZ7U7WLZRJXBZU3T","bundle":"https://pith.science/pith/IISMFZKXHZMZ7U7WLZRJXBZU3T/bundle.json","state":"https://pith.science/pith/IISMFZKXHZMZ7U7WLZRJXBZU3T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IISMFZKXHZMZ7U7WLZRJXBZU3T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IISMFZKXHZMZ7U7WLZRJXBZU3T","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":"2d582129c6966ac32a670233e063ae2b8c5d71ed2842e566ea7546a1cd300a53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T23:22:59Z","title_canon_sha256":"054b205077094781ce0f36b2d452c7d82de3e7d5b06e2c6c2420d1616487c79e"},"schema_version":"1.0","source":{"id":"2605.24773","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24773","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24773v1","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24773","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"pith_short_12","alias_value":"IISMFZKXHZMZ","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"pith_short_16","alias_value":"IISMFZKXHZMZ7U7W","created_at":"2026-05-26T01:03:57Z"},{"alias_kind":"pith_short_8","alias_value":"IISMFZKX","created_at":"2026-05-26T01:03:57Z"}],"graph_snapshots":[{"event_id":"sha256:310e117c7c5d94b8606dcae528cb76daf70a445fe445878e2d25a62ba9d73fa3","target":"graph","created_at":"2026-05-26T01:03:57Z","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/2605.24773/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Annotator disagreement in emotion classification reflects ambiguity intrinsic to emotion concepts and is essential for predictor-quality assessment in subjective NLP. Yet no prior work integrates soft-label learning with Bayesian deep learning to evaluate uncertainty along axes including annotator-distribution fidelity. We train a linear head on a frozen RoBERTa via cyclical stochastic gradient Markov chain Monte Carlo (cSG-MCMC), targeting the empirical annotator distribution with a soft-label objective under a five-axis evaluation. On the 28-emotion GoEmotions benchmark, the proposed method ","authors_text":"Keito Inoshita, Takato Ueno","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T23:22:59Z","title":"Uncertainty Decomposition via Cyclical SG-MCMC and Soft-label Learning for Subjective NLP"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24773","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":"sha256:8e600f48f45d4034419d8e7f8df1f22e64239fa1b741262629bc4d5d69c10097","target":"record","created_at":"2026-05-26T01:03:57Z","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":"2d582129c6966ac32a670233e063ae2b8c5d71ed2842e566ea7546a1cd300a53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T23:22:59Z","title_canon_sha256":"054b205077094781ce0f36b2d452c7d82de3e7d5b06e2c6c2420d1616487c79e"},"schema_version":"1.0","source":{"id":"2605.24773","kind":"arxiv","version":1}},"canonical_sha256":"4224c2e5573e599fd3f65e629b8734dcc36a2562c4810a76a757b6a64c7f3693","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4224c2e5573e599fd3f65e629b8734dcc36a2562c4810a76a757b6a64c7f3693","first_computed_at":"2026-05-26T01:03:57.481802Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:03:57.481802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uVkCMIfmHPLjABCxc4XC1wTUeNXnZ92AwIMvbAL9cAa1LFbjPnSm1vB09GnINSq/VskLGpqj5Uyv0kouqVQjAw==","signature_status":"signed_v1","signed_at":"2026-05-26T01:03:57.482689Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.24773","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e600f48f45d4034419d8e7f8df1f22e64239fa1b741262629bc4d5d69c10097","sha256:310e117c7c5d94b8606dcae528cb76daf70a445fe445878e2d25a62ba9d73fa3"],"state_sha256":"b7ee46de06beda4eca247b33f3ecdf8adb4580d9be665e5218385d2f04a45932"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cZneDEWPtV7JPqlwj5sCy7JNyoZdAex6iyHkKgRB4comGIdC9pZPwNy2ZF/Rollem7shVkz6r4cFqnotsl5/Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T20:09:49.676716Z","bundle_sha256":"aab361fd8ab74894c534033f946dfd9a181e7e8c3575d8579f1926addee272ed"}}