{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IJM7G4B63TIG7QGUQNOSIPFROR","short_pith_number":"pith:IJM7G4B6","schema_version":"1.0","canonical_sha256":"4259f3703edcd06fc0d4835d243cb17445c878d9ae9f29fae46226ca5bc47ba1","source":{"kind":"arxiv","id":"2606.21963","version":1},"attestation_state":"computed","paper":{"title":"Holmes: Multimodal Agentic Diagnosis for Mixed-Language Mobile Crashes at Industrial Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.AI","authors_text":"Haibin Zheng, Jia Li, Ting Peng, Wenyuan Ma, Yuetang Deng","submitted_at":"2026-06-20T09:31:26Z","abstract_excerpt":"Diagnosing mobile crashes in ultra-large-scale industrial applications is a formidable challenge due to the sheer volume of code, the complexity of mixed-language environments, and the inability to reproduce failures locally. Traditional static analysis struggles with scalability, while existing LLM-based agents often rely on reproducible environments unavailable in post-mortem scenarios. We present Holmes, a multi-agent system that automates root cause analysis by synthesizing multimodal runtime signals--stack traces, logs, and thread states--to reconstruct failure contexts without reproducti"},"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":"2606.21963","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-20T09:31:26Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"5b876a538638b983143d6b69fda7f9237481244b7b029af7be1e61beae10914a","abstract_canon_sha256":"52fc8db68f003b65e43590f5d8c2442d3b5d60fb55e0b99b3c86c4ea7810b0d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:04.345869Z","signature_b64":"2384EvNh6f+GrSoi9YZmzDhqv+6/JHlErC+uBWdMYSSwfIMEP5dVLANZv5udbiYSdZL6JuASU1qE1m/EMpyQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4259f3703edcd06fc0d4835d243cb17445c878d9ae9f29fae46226ca5bc47ba1","last_reissued_at":"2026-06-23T02:13:04.345521Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:04.345521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Holmes: Multimodal Agentic Diagnosis for Mixed-Language Mobile Crashes at Industrial Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.AI","authors_text":"Haibin Zheng, Jia Li, Ting Peng, Wenyuan Ma, Yuetang Deng","submitted_at":"2026-06-20T09:31:26Z","abstract_excerpt":"Diagnosing mobile crashes in ultra-large-scale industrial applications is a formidable challenge due to the sheer volume of code, the complexity of mixed-language environments, and the inability to reproduce failures locally. Traditional static analysis struggles with scalability, while existing LLM-based agents often rely on reproducible environments unavailable in post-mortem scenarios. We present Holmes, a multi-agent system that automates root cause analysis by synthesizing multimodal runtime signals--stack traces, logs, and thread states--to reconstruct failure contexts without reproducti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21963","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/2606.21963/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":"2606.21963","created_at":"2026-06-23T02:13:04.345585+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21963v1","created_at":"2026-06-23T02:13:04.345585+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21963","created_at":"2026-06-23T02:13:04.345585+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJM7G4B63TIG","created_at":"2026-06-23T02:13:04.345585+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJM7G4B63TIG7QGU","created_at":"2026-06-23T02:13:04.345585+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJM7G4B6","created_at":"2026-06-23T02:13:04.345585+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/IJM7G4B63TIG7QGUQNOSIPFROR","json":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR.json","graph_json":"https://pith.science/api/pith-number/IJM7G4B63TIG7QGUQNOSIPFROR/graph.json","events_json":"https://pith.science/api/pith-number/IJM7G4B63TIG7QGUQNOSIPFROR/events.json","paper":"https://pith.science/paper/IJM7G4B6"},"agent_actions":{"view_html":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR","download_json":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR.json","view_paper":"https://pith.science/paper/IJM7G4B6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21963&json=true","fetch_graph":"https://pith.science/api/pith-number/IJM7G4B63TIG7QGUQNOSIPFROR/graph.json","fetch_events":"https://pith.science/api/pith-number/IJM7G4B63TIG7QGUQNOSIPFROR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR/action/storage_attestation","attest_author":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR/action/author_attestation","sign_citation":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR/action/citation_signature","submit_replication":"https://pith.science/pith/IJM7G4B63TIG7QGUQNOSIPFROR/action/replication_record"}},"created_at":"2026-06-23T02:13:04.345585+00:00","updated_at":"2026-06-23T02:13:04.345585+00:00"}