{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XNGZWU72WT2B6UL6TS7RNOZPDB","short_pith_number":"pith:XNGZWU72","schema_version":"1.0","canonical_sha256":"bb4d9b53fab4f41f517e9cbf16bb2f185cf81b1184c506e7628a05e4fdce73a0","source":{"kind":"arxiv","id":"2511.17505","version":1},"attestation_state":"computed","paper":{"title":"Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Chenhua Shi, Jayanta Choudhury, Joji Philip, Subhadip Bandyopadhyay","submitted_at":"2025-10-02T14:34:59Z","abstract_excerpt":"To keep modern Radio Access Networks (RAN) running smoothly, operators need to spot the real-world triggers behind Service-Level Agreement (SLA) breaches well before customers feel them. We introduce an AI/ML pipeline that does two things most tools miss: (1) finds the likely root-cause indicators and (2) reveals the exact order in which those events unfold. We start by labeling network data: records linked to past SLA breaches are marked `abnormal', and everything else `normal'. Our model then learns the causal chain that turns normal behavior into a fault. In Monte Carlo tests the approach p"},"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":"2511.17505","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2025-10-02T14:34:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f8bee236a1b3f9e72966f45ad812b6b55fdb7c865cb7aeecd3fd65e1408da992","abstract_canon_sha256":"cf284d6d62dd49943491aa2a2cf12d2eee6f2ab57143813c7920c52442315b79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-15T00:21:14.399344Z","signature_b64":"1liCTJlG58JjONozay2EVicLpRfBBMikG3vDisYjhWKgqD4kCQle7tpIXnoT5ZzzEi3/zo8Wt9dVUMiAlb0/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb4d9b53fab4f41f517e9cbf16bb2f185cf81b1184c506e7628a05e4fdce73a0","last_reissued_at":"2026-07-15T00:21:14.398332Z","signature_status":"signed_v1","first_computed_at":"2026-07-15T00:21:14.398332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NI","authors_text":"Chenhua Shi, Jayanta Choudhury, Joji Philip, Subhadip Bandyopadhyay","submitted_at":"2025-10-02T14:34:59Z","abstract_excerpt":"To keep modern Radio Access Networks (RAN) running smoothly, operators need to spot the real-world triggers behind Service-Level Agreement (SLA) breaches well before customers feel them. We introduce an AI/ML pipeline that does two things most tools miss: (1) finds the likely root-cause indicators and (2) reveals the exact order in which those events unfold. We start by labeling network data: records linked to past SLA breaches are marked `abnormal', and everything else `normal'. Our model then learns the causal chain that turns normal behavior into a fault. In Monte Carlo tests the approach p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.17505","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/2511.17505/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":"2511.17505","created_at":"2026-07-15T00:21:14.398803+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.17505v1","created_at":"2026-07-15T00:21:14.398803+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.17505","created_at":"2026-07-15T00:21:14.398803+00:00"},{"alias_kind":"pith_short_12","alias_value":"XNGZWU72WT2B","created_at":"2026-07-15T00:21:14.398803+00:00"},{"alias_kind":"pith_short_16","alias_value":"XNGZWU72WT2B6UL6","created_at":"2026-07-15T00:21:14.398803+00:00"},{"alias_kind":"pith_short_8","alias_value":"XNGZWU72","created_at":"2026-07-15T00:21:14.398803+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/XNGZWU72WT2B6UL6TS7RNOZPDB","json":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB.json","graph_json":"https://pith.science/api/pith-number/XNGZWU72WT2B6UL6TS7RNOZPDB/graph.json","events_json":"https://pith.science/api/pith-number/XNGZWU72WT2B6UL6TS7RNOZPDB/events.json","paper":"https://pith.science/paper/XNGZWU72"},"agent_actions":{"view_html":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB","download_json":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB.json","view_paper":"https://pith.science/paper/XNGZWU72","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.17505&json=true","fetch_graph":"https://pith.science/api/pith-number/XNGZWU72WT2B6UL6TS7RNOZPDB/graph.json","fetch_events":"https://pith.science/api/pith-number/XNGZWU72WT2B6UL6TS7RNOZPDB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB/action/storage_attestation","attest_author":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB/action/author_attestation","sign_citation":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB/action/citation_signature","submit_replication":"https://pith.science/pith/XNGZWU72WT2B6UL6TS7RNOZPDB/action/replication_record"}},"created_at":"2026-07-15T00:21:14.398803+00:00","updated_at":"2026-07-15T00:21:14.398803+00:00"}