{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OVKV4BGOG45EOIEZRBXXMZEAR3","short_pith_number":"pith:OVKV4BGO","schema_version":"1.0","canonical_sha256":"75555e04ce373a472099886f7664808ecb0563108499f3f4353412e97950387d","source":{"kind":"arxiv","id":"2504.21198","version":2},"attestation_state":"computed","paper":{"title":"Graph Synthetic Out-of-Distribution Exposure with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haoyan Xu, Mengyuan Li, Xiyang Hu, Yue Zhao, Zhan Cheng, Zhengtao Yao, Ziyi Wang","submitted_at":"2025-04-29T22:04:30Z","abstract_excerpt":"Out-of-distribution (OOD) detection in graphs is critical for ensuring model robustness in open-world and safety-sensitive applications. Existing graph OOD detection approaches typically train an in-distribution (ID) classifier on ID data alone, then apply post-hoc scoring to detect OOD instances. While OOD exposure - adding auxiliary OOD samples during training - can improve detection, current graph-based methods often assume access to real OOD nodes, which is often impractical or costly. In this paper, we present GOE-LLM, a framework that leverages Large Language Models (LLMs) to achieve OOD"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.21198","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-29T22:04:30Z","cross_cats_sorted":[],"title_canon_sha256":"cc21a44f9975ad739271e733767256b345baaadae8f45bd0715babf9e03ae68d","abstract_canon_sha256":"06b4f2e571fb5737db151929859304b7125e46df046553721715e878c7c43616"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:22.016667Z","signature_b64":"6rp4UD1aWFktHoh8f8J3WLyuOBVnH0GC1MX9yo5aF/nqPwqUY/EjejxlqZAYiiJyrUnovkKwX6yglV6t/PtkDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75555e04ce373a472099886f7664808ecb0563108499f3f4353412e97950387d","last_reissued_at":"2026-07-05T11:04:22.016162Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:22.016162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Synthetic Out-of-Distribution Exposure with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Haoyan Xu, Mengyuan Li, Xiyang Hu, Yue Zhao, Zhan Cheng, Zhengtao Yao, Ziyi Wang","submitted_at":"2025-04-29T22:04:30Z","abstract_excerpt":"Out-of-distribution (OOD) detection in graphs is critical for ensuring model robustness in open-world and safety-sensitive applications. Existing graph OOD detection approaches typically train an in-distribution (ID) classifier on ID data alone, then apply post-hoc scoring to detect OOD instances. While OOD exposure - adding auxiliary OOD samples during training - can improve detection, current graph-based methods often assume access to real OOD nodes, which is often impractical or costly. In this paper, we present GOE-LLM, a framework that leverages Large Language Models (LLMs) to achieve OOD"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.21198","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/2504.21198/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.21198","created_at":"2026-07-05T11:04:22.016226+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.21198v2","created_at":"2026-07-05T11:04:22.016226+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.21198","created_at":"2026-07-05T11:04:22.016226+00:00"},{"alias_kind":"pith_short_12","alias_value":"OVKV4BGOG45E","created_at":"2026-07-05T11:04:22.016226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OVKV4BGOG45EOIEZ","created_at":"2026-07-05T11:04:22.016226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OVKV4BGO","created_at":"2026-07-05T11:04:22.016226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22741","citing_title":"GRADE: Graph Representation of LLM Agent Dependency and Execution","ref_index":107,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3","json":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3.json","graph_json":"https://pith.science/api/pith-number/OVKV4BGOG45EOIEZRBXXMZEAR3/graph.json","events_json":"https://pith.science/api/pith-number/OVKV4BGOG45EOIEZRBXXMZEAR3/events.json","paper":"https://pith.science/paper/OVKV4BGO"},"agent_actions":{"view_html":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3","download_json":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3.json","view_paper":"https://pith.science/paper/OVKV4BGO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.21198&json=true","fetch_graph":"https://pith.science/api/pith-number/OVKV4BGOG45EOIEZRBXXMZEAR3/graph.json","fetch_events":"https://pith.science/api/pith-number/OVKV4BGOG45EOIEZRBXXMZEAR3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3/action/storage_attestation","attest_author":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3/action/author_attestation","sign_citation":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3/action/citation_signature","submit_replication":"https://pith.science/pith/OVKV4BGOG45EOIEZRBXXMZEAR3/action/replication_record"}},"created_at":"2026-07-05T11:04:22.016226+00:00","updated_at":"2026-07-05T11:04:22.016226+00:00"}