{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:TYGXJVSTJO723J2IS2JEQXJILD","short_pith_number":"pith:TYGXJVST","canonical_record":{"source":{"id":"2412.00481","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2024-11-30T13:40:54Z","cross_cats_sorted":[],"title_canon_sha256":"55c97c858ccc52585403096cfc44abef0b73758387cce05bdcf090ae2e024502","abstract_canon_sha256":"a7821976e082ccb9f979f5c381d4c003ec48ba07a007e84abe31cfc65e32006c"},"schema_version":"1.0"},"canonical_sha256":"9e0d74d6534bbfada7489692485d2858f8fad5f6f0b75045b2176f663842b0f6","source":{"kind":"arxiv","id":"2412.00481","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.00481","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"arxiv_version","alias_value":"2412.00481v1","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.00481","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"pith_short_12","alias_value":"TYGXJVSTJO72","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"pith_short_16","alias_value":"TYGXJVSTJO723J2I","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"pith_short_8","alias_value":"TYGXJVST","created_at":"2026-07-05T09:42:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:TYGXJVSTJO723J2IS2JEQXJILD","target":"record","payload":{"canonical_record":{"source":{"id":"2412.00481","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2024-11-30T13:40:54Z","cross_cats_sorted":[],"title_canon_sha256":"55c97c858ccc52585403096cfc44abef0b73758387cce05bdcf090ae2e024502","abstract_canon_sha256":"a7821976e082ccb9f979f5c381d4c003ec48ba07a007e84abe31cfc65e32006c"},"schema_version":"1.0"},"canonical_sha256":"9e0d74d6534bbfada7489692485d2858f8fad5f6f0b75045b2176f663842b0f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:42.199343Z","signature_b64":"j8EqlF5sOM9jBR/jKYOwGHxxvgQA03SxBWislz3V8DAgL5Ek55LktC30GvyxMQdssrvdn1+K+xcsKY7ZewPlBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e0d74d6534bbfada7489692485d2858f8fad5f6f0b75045b2176f663842b0f6","last_reissued_at":"2026-07-05T09:42:42.198911Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:42.198911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.00481","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:42:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2DQ/nemHA5UGj6Q0qUSXzrtdYKH9YTyv+TXKjL2aE0PpT5bdp94aYw/GkDtwkewtyq4Y53qkDQ+ONmm3qBMkDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T00:47:28.624710Z"},"content_sha256":"6212e70eb8b57e45a53c0eae75a9b575976e121698405523d93be3da6719eb2b","schema_version":"1.0","event_id":"sha256:6212e70eb8b57e45a53c0eae75a9b575976e121698405523d93be3da6719eb2b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:TYGXJVSTJO723J2IS2JEQXJILD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MaintAGT:Sim2Real-Guided Multimodal Large Model for Intelligent Maintenance with Chain-of-Thought Reasoning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Feibin Zhang, Fulei Chu, Hongliang He, Jinfeng Huang, Kangding Yang, Li Meng, Qi Li, Xu Wang","submitted_at":"2024-11-30T13:40:54Z","abstract_excerpt":"In recent years, large language models have made significant advancements in the field of natural language processing, yet there are still inadequacies in specific domain knowledge and applications. This paper Proposes MaintAGT, a professional large model for intelligent operations and maintenance, aimed at addressing this issue. The system comprises three key components: a signal-to-text model, a pure text model, and a multimodal model. Firstly, the signal-to-text model was designed to convert raw signal data into textual descriptions, bridging the gap between signal data and text-based analy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.00481","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/2412.00481/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:42:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"11NwqN1cYNY7oTtEq2lp0vJXulHRBIY6zwvGXD+ULVdeeNkocSnyyANq+2wU7vAjMxfFquQ/RJ4QZz46qCxnDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T00:47:28.625371Z"},"content_sha256":"eded4492255d23638958290845b87db0572b1fda4ba47811c0d07bc915f7ab1e","schema_version":"1.0","event_id":"sha256:eded4492255d23638958290845b87db0572b1fda4ba47811c0d07bc915f7ab1e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TYGXJVSTJO723J2IS2JEQXJILD/bundle.json","state_url":"https://pith.science/pith/TYGXJVSTJO723J2IS2JEQXJILD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TYGXJVSTJO723J2IS2JEQXJILD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T00:47:28Z","links":{"resolver":"https://pith.science/pith/TYGXJVSTJO723J2IS2JEQXJILD","bundle":"https://pith.science/pith/TYGXJVSTJO723J2IS2JEQXJILD/bundle.json","state":"https://pith.science/pith/TYGXJVSTJO723J2IS2JEQXJILD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TYGXJVSTJO723J2IS2JEQXJILD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TYGXJVSTJO723J2IS2JEQXJILD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a7821976e082ccb9f979f5c381d4c003ec48ba07a007e84abe31cfc65e32006c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2024-11-30T13:40:54Z","title_canon_sha256":"55c97c858ccc52585403096cfc44abef0b73758387cce05bdcf090ae2e024502"},"schema_version":"1.0","source":{"id":"2412.00481","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.00481","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"arxiv_version","alias_value":"2412.00481v1","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.00481","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"pith_short_12","alias_value":"TYGXJVSTJO72","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"pith_short_16","alias_value":"TYGXJVSTJO723J2I","created_at":"2026-07-05T09:42:42Z"},{"alias_kind":"pith_short_8","alias_value":"TYGXJVST","created_at":"2026-07-05T09:42:42Z"}],"graph_snapshots":[{"event_id":"sha256:eded4492255d23638958290845b87db0572b1fda4ba47811c0d07bc915f7ab1e","target":"graph","created_at":"2026-07-05T09:42:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.00481/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, large language models have made significant advancements in the field of natural language processing, yet there are still inadequacies in specific domain knowledge and applications. This paper Proposes MaintAGT, a professional large model for intelligent operations and maintenance, aimed at addressing this issue. The system comprises three key components: a signal-to-text model, a pure text model, and a multimodal model. Firstly, the signal-to-text model was designed to convert raw signal data into textual descriptions, bridging the gap between signal data and text-based analy","authors_text":"Feibin Zhang, Fulei Chu, Hongliang He, Jinfeng Huang, Kangding Yang, Li Meng, Qi Li, Xu Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2024-11-30T13:40:54Z","title":"MaintAGT:Sim2Real-Guided Multimodal Large Model for Intelligent Maintenance with Chain-of-Thought Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.00481","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6212e70eb8b57e45a53c0eae75a9b575976e121698405523d93be3da6719eb2b","target":"record","created_at":"2026-07-05T09:42:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a7821976e082ccb9f979f5c381d4c003ec48ba07a007e84abe31cfc65e32006c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2024-11-30T13:40:54Z","title_canon_sha256":"55c97c858ccc52585403096cfc44abef0b73758387cce05bdcf090ae2e024502"},"schema_version":"1.0","source":{"id":"2412.00481","kind":"arxiv","version":1}},"canonical_sha256":"9e0d74d6534bbfada7489692485d2858f8fad5f6f0b75045b2176f663842b0f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e0d74d6534bbfada7489692485d2858f8fad5f6f0b75045b2176f663842b0f6","first_computed_at":"2026-07-05T09:42:42.198911Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:42:42.198911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j8EqlF5sOM9jBR/jKYOwGHxxvgQA03SxBWislz3V8DAgL5Ek55LktC30GvyxMQdssrvdn1+K+xcsKY7ZewPlBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:42:42.199343Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.00481","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6212e70eb8b57e45a53c0eae75a9b575976e121698405523d93be3da6719eb2b","sha256:eded4492255d23638958290845b87db0572b1fda4ba47811c0d07bc915f7ab1e"],"state_sha256":"3507e62d0b9fe0adb397b3cdc7a221f78f9b0ebab9677559bce1295134b29e2a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mXnDi893JWtvkuwVk8NeL1qM5Oa8hAmzMxwr/Gne/TSBC1/V7JhgGuSRhu0bdWGbr5Uwvi0PWfVx0+DOFAgGAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T00:47:28.631344Z","bundle_sha256":"b8a8f2886316c5b57592ab669d8689e593cc3d9f7d1a6e1639a3b1f9be302df4"}}