{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:63CYIUW7HMLME2O5GGZ4WBLDCV","short_pith_number":"pith:63CYIUW7","schema_version":"1.0","canonical_sha256":"f6c58452df3b16c269dd31b3cb0563156e35a9d6161fdcd22b7bf4e9d832d3b6","source":{"kind":"arxiv","id":"2606.00582","version":1},"attestation_state":"computed","paper":{"title":"PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fengxiao Tang, Ming Zhao, Nei Kato, Zongzong Wu","submitted_at":"2026-05-30T07:18:42Z","abstract_excerpt":"Network faults propagate layer by layer along topology and protocol dependencies, yet operations systems typically observe only symptomatic alerts at the tail end of propagation chains, where distinct root-cause faults may produce highly similar end-point symptoms. Existing approaches, whether rule-based, machine learning (ML)-based, or large language model (LLM)-based, fundamentally map the alert set to a diagnosis in a single pass and are structurally incapable of resolving this end-point ambiguity. This paper proposes PropLLM, which is the first to integrate the hop-by-hop scene reconstruct"},"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":"2606.00582","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-30T07:18:42Z","cross_cats_sorted":[],"title_canon_sha256":"d9a9630bbd2bf55f91473feb810b695a7a67ae80d7dbcecfd275273a9d8df375","abstract_canon_sha256":"f105f6109057dc0346c69d54e11ca926a4e788529a9a45badf187e7ed9c31098"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:58.901425Z","signature_b64":"2xTI1vu86NC1nH/zs2BlI3bFAbQCl0hucCHoqtQR/hYtKnPJVLP8bn/QmfHMfxekFvu2IiSKRwVrpY5hVR2gBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6c58452df3b16c269dd31b3cb0563156e35a9d6161fdcd22b7bf4e9d832d3b6","last_reissued_at":"2026-06-02T01:03:58.901029Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:58.901029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fengxiao Tang, Ming Zhao, Nei Kato, Zongzong Wu","submitted_at":"2026-05-30T07:18:42Z","abstract_excerpt":"Network faults propagate layer by layer along topology and protocol dependencies, yet operations systems typically observe only symptomatic alerts at the tail end of propagation chains, where distinct root-cause faults may produce highly similar end-point symptoms. Existing approaches, whether rule-based, machine learning (ML)-based, or large language model (LLM)-based, fundamentally map the alert set to a diagnosis in a single pass and are structurally incapable of resolving this end-point ambiguity. This paper proposes PropLLM, which is the first to integrate the hop-by-hop scene reconstruct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00582","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/2606.00582/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":"2606.00582","created_at":"2026-06-02T01:03:58.901083+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.00582v1","created_at":"2026-06-02T01:03:58.901083+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00582","created_at":"2026-06-02T01:03:58.901083+00:00"},{"alias_kind":"pith_short_12","alias_value":"63CYIUW7HMLM","created_at":"2026-06-02T01:03:58.901083+00:00"},{"alias_kind":"pith_short_16","alias_value":"63CYIUW7HMLME2O5","created_at":"2026-06-02T01:03:58.901083+00:00"},{"alias_kind":"pith_short_8","alias_value":"63CYIUW7","created_at":"2026-06-02T01:03:58.901083+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV","json":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV.json","graph_json":"https://pith.science/api/pith-number/63CYIUW7HMLME2O5GGZ4WBLDCV/graph.json","events_json":"https://pith.science/api/pith-number/63CYIUW7HMLME2O5GGZ4WBLDCV/events.json","paper":"https://pith.science/paper/63CYIUW7"},"agent_actions":{"view_html":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV","download_json":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV.json","view_paper":"https://pith.science/paper/63CYIUW7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.00582&json=true","fetch_graph":"https://pith.science/api/pith-number/63CYIUW7HMLME2O5GGZ4WBLDCV/graph.json","fetch_events":"https://pith.science/api/pith-number/63CYIUW7HMLME2O5GGZ4WBLDCV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV/action/storage_attestation","attest_author":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV/action/author_attestation","sign_citation":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV/action/citation_signature","submit_replication":"https://pith.science/pith/63CYIUW7HMLME2O5GGZ4WBLDCV/action/replication_record"}},"created_at":"2026-06-02T01:03:58.901083+00:00","updated_at":"2026-06-02T01:03:58.901083+00:00"}