{"paper":{"title":"Cross-Domain Query Translation for Network Troubleshooting: A Multi-Agent LLM Framework with Privacy Preservation and Self-Reflection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Multi-agent LLM system classifies network queries, anonymizes PII, and translates expert advice for users.","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Brigitte Jaumard, Karthikeyan Premkumar, Nguyen Phuc Tran, Salman Memon","submitted_at":"2026-04-14T23:23:46Z","abstract_excerpt":"This paper presents a hierarchical multi-agent LLM architecture to bridge communication gaps between non-technical end users and telecommunications domain experts in private network environments. We propose a cross-domain query translation framework that leverages specialized language models coordinated through multi-agent reflection-based reasoning. The resulting system addresses three critical challenges: (1) accurately classify user queries related to telecommunications network issues using a dual-stage hierarchical approach, (2) preserve user privacy through the anonymization of semantical"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The resulting system addresses three critical challenges: (1) accurately classify user queries related to telecommunications network issues using a dual-stage hierarchical approach, (2) preserve user privacy through the anonymization of semantically relevant personally identifiable information (PII) while maintaining diagnostic utility, and (3) translate technical expert responses into user-comprehensible language.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the proposed semantic-preserving anonymization techniques can simultaneously respect k-anonymity and differential privacy principles while retaining enough diagnostic utility for accurate expert responses, and that the multi-agent self-reflection loop will reliably improve outputs without introducing new errors.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A hierarchical multi-agent LLM framework with self-reflection and semantic-preserving anonymization translates cross-domain queries for network troubleshooting and was evaluated on 10,000 unseen scenarios.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Multi-agent LLM system classifies network queries, anonymizes PII, and translates expert advice for users.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"bdb07f74fda103249710d23f82244fde3a78c23918cd50d4a5d5dcb7351b413e"},"source":{"id":"2604.13353","kind":"arxiv","version":2},"verdict":{"id":"c52b7c4b-b330-40c6-91d2-008d4c18d060","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T13:41:22.608740Z","strongest_claim":"The resulting system addresses three critical challenges: (1) accurately classify user queries related to telecommunications network issues using a dual-stage hierarchical approach, (2) preserve user privacy through the anonymization of semantically relevant personally identifiable information (PII) while maintaining diagnostic utility, and (3) translate technical expert responses into user-comprehensible language.","one_line_summary":"A hierarchical multi-agent LLM framework with self-reflection and semantic-preserving anonymization translates cross-domain queries for network troubleshooting and was evaluated on 10,000 unseen scenarios.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the proposed semantic-preserving anonymization techniques can simultaneously respect k-anonymity and differential privacy principles while retaining enough diagnostic utility for accurate expert responses, and that the multi-agent self-reflection loop will reliably improve outputs without introducing new errors.","pith_extraction_headline":"Multi-agent LLM system classifies network queries, anonymizes PII, and translates expert advice for users."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.13353/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"}