{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MKGSNZOQQSKZR25K2OGVEMLRQG","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":"0ada5088ff7c8d2244554e8de46bba31981bcb15afdb2c989fe1d7efb6491d51","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2026-06-10T05:12:29Z","title_canon_sha256":"bc9a36c96fa00b0e9659131bfeaf7e1c8df2b58e79915a7c3334ce8f6f69542f"},"schema_version":"1.0","source":{"id":"2606.11663","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11663","created_at":"2026-06-11T01:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11663v1","created_at":"2026-06-11T01:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11663","created_at":"2026-06-11T01:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"MKGSNZOQQSKZ","created_at":"2026-06-11T01:10:01Z"},{"alias_kind":"pith_short_16","alias_value":"MKGSNZOQQSKZR25K","created_at":"2026-06-11T01:10:01Z"},{"alias_kind":"pith_short_8","alias_value":"MKGSNZOQ","created_at":"2026-06-11T01:10:01Z"}],"graph_snapshots":[{"event_id":"sha256:157799327f2bdeeb1b5a91ddeb567563ee6c53deaf58742316bc8bb3c09d4c3a","target":"graph","created_at":"2026-06-11T01:10:01Z","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/2606.11663/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets. Existing approaches predominantly yield a single point estimate and treat job attributes such as location, occupation, and industry as independent categorical features, ignoring both the inherent uncertainty and multi-modality of real-world compensation data and the rich hierarchical and semantic-similarity relationships that govern pay norms. In this paper we propose GAT-MDN, a unified framework that addresses both limitations simultaneously. For each of the three","authors_text":"F.W. Takes, Mohammad Shokri, N. van Weeren, Zhipei Qin","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2026-06-10T05:12:29Z","title":"Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11663","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:b772017fc55dca99b85827a66d59082f7fe114fc8f2c0182c219a46f7790f97e","target":"record","created_at":"2026-06-11T01:10:01Z","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":"0ada5088ff7c8d2244554e8de46bba31981bcb15afdb2c989fe1d7efb6491d51","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2026-06-10T05:12:29Z","title_canon_sha256":"bc9a36c96fa00b0e9659131bfeaf7e1c8df2b58e79915a7c3334ce8f6f69542f"},"schema_version":"1.0","source":{"id":"2606.11663","kind":"arxiv","version":1}},"canonical_sha256":"628d26e5d0849598ebaad38d52317181a4dd65c224be64dec18059e2ddb85a22","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"628d26e5d0849598ebaad38d52317181a4dd65c224be64dec18059e2ddb85a22","first_computed_at":"2026-06-11T01:10:01.652057Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:10:01.652057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GruJPxvq0SRT52WMnXZ8VhvBAIUeoqErgOpOcQ9xCXJ/FraQV+5Q4Wo3XIMb3fvbagXiJJ5aqDRBSDmk2/jIDQ==","signature_status":"signed_v1","signed_at":"2026-06-11T01:10:01.652854Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.11663","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b772017fc55dca99b85827a66d59082f7fe114fc8f2c0182c219a46f7790f97e","sha256:157799327f2bdeeb1b5a91ddeb567563ee6c53deaf58742316bc8bb3c09d4c3a"],"state_sha256":"bb5a21010bf413294a7fe0aa835c27ac1829477b3ecc096e846a9c31470377a9"}