{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:A756ZO2DWY56TTVHCQBVLFIREU","short_pith_number":"pith:A756ZO2D","canonical_record":{"source":{"id":"2602.13807","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-14T14:35:34Z","cross_cats_sorted":[],"title_canon_sha256":"0244569d24ff91c6c5ffe7fb719f8af2d4e8635e10a05e66955e1234ffadd402","abstract_canon_sha256":"4424ab4dcfbc87387a81d0e9e4087a140aa4e5e42441423e76d98206e77d7fab"},"schema_version":"1.0"},"canonical_sha256":"07fbecbb43b63be9cea7140355951125054cc50b5d7c8e5f2fbe3830e9e13bf5","source":{"kind":"arxiv","id":"2602.13807","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.13807","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"2602.13807v2","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.13807","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"A756ZO2DWY56","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"A756ZO2DWY56TTVH","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"A756ZO2D","created_at":"2026-06-10T01:09:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:A756ZO2DWY56TTVHCQBVLFIREU","target":"record","payload":{"canonical_record":{"source":{"id":"2602.13807","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-14T14:35:34Z","cross_cats_sorted":[],"title_canon_sha256":"0244569d24ff91c6c5ffe7fb719f8af2d4e8635e10a05e66955e1234ffadd402","abstract_canon_sha256":"4424ab4dcfbc87387a81d0e9e4087a140aa4e5e42441423e76d98206e77d7fab"},"schema_version":"1.0"},"canonical_sha256":"07fbecbb43b63be9cea7140355951125054cc50b5d7c8e5f2fbe3830e9e13bf5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:09:56.234043Z","signature_b64":"wc+ZpQuI+JlWN15zf/cegDYf4l6Ww2QrJN77FvmIJSK1/x32iOoD3qk1zMWjF/yksQtVva5Caan2SUtvd/6cBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07fbecbb43b63be9cea7140355951125054cc50b5d7c8e5f2fbe3830e9e13bf5","last_reissued_at":"2026-06-10T01:09:56.232869Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:09:56.232869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.13807","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-10T01:09:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YTc/w132NArYYOqhyG0nda6McGj0pKlZm6rfkmVVNmX7D4yJ6QfRXhIqSTRfyiWgVbxHG+4PWF+vTWWI8kmxDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T09:34:34.204706Z"},"content_sha256":"6d4a2294543e4ddc1fcb925f888bae143c0d8bcb0a1260b1e3f2cb92be2dd060","schema_version":"1.0","event_id":"sha256:6d4a2294543e4ddc1fcb925f888bae143c0d8bcb0a1260b1e3f2cb92be2dd060"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:A756ZO2DWY56TTVHCQBVLFIREU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Mingyue Cheng, Tian Gao, Xiaoyu Tao, Yuchong Wu, Ze Guo","submitted_at":"2026-02-14T14:35:34Z","abstract_excerpt":"Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings. However, most existing methods frame anomaly detection as a purely discriminative prediction task with fixed feature representations, rather than an evidence-driven diagnostic process. As a result, they often struggle when anomalies exhibit strong context dependence, diverse patterns, or domain shifts across datasets. To address these challenges, we propose AnomaMind, an agentic time series anomaly dete"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.13807","kind":"arxiv","version":2},"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/2602.13807/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":"2026-06-10T01:09:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H7hRmx3SgW+wL+PI57SArg5lVt3esdEZlWT/+gdVPinVBwU5YubxGRT8ALpsZ/i6VTsH3UBO+mxSH+X6Pl9VBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T09:34:34.205691Z"},"content_sha256":"4a1a5400d5726b305ca034fb7ee650ace70211d0bcbe8e9bb5982985cf04bf32","schema_version":"1.0","event_id":"sha256:4a1a5400d5726b305ca034fb7ee650ace70211d0bcbe8e9bb5982985cf04bf32"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A756ZO2DWY56TTVHCQBVLFIREU/bundle.json","state_url":"https://pith.science/pith/A756ZO2DWY56TTVHCQBVLFIREU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A756ZO2DWY56TTVHCQBVLFIREU/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":"2026-06-10T09:34:34Z","links":{"resolver":"https://pith.science/pith/A756ZO2DWY56TTVHCQBVLFIREU","bundle":"https://pith.science/pith/A756ZO2DWY56TTVHCQBVLFIREU/bundle.json","state":"https://pith.science/pith/A756ZO2DWY56TTVHCQBVLFIREU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A756ZO2DWY56TTVHCQBVLFIREU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:A756ZO2DWY56TTVHCQBVLFIREU","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":"4424ab4dcfbc87387a81d0e9e4087a140aa4e5e42441423e76d98206e77d7fab","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-14T14:35:34Z","title_canon_sha256":"0244569d24ff91c6c5ffe7fb719f8af2d4e8635e10a05e66955e1234ffadd402"},"schema_version":"1.0","source":{"id":"2602.13807","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.13807","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"2602.13807v2","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.13807","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"A756ZO2DWY56","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"A756ZO2DWY56TTVH","created_at":"2026-06-10T01:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"A756ZO2D","created_at":"2026-06-10T01:09:56Z"}],"graph_snapshots":[{"event_id":"sha256:4a1a5400d5726b305ca034fb7ee650ace70211d0bcbe8e9bb5982985cf04bf32","target":"graph","created_at":"2026-06-10T01:09:56Z","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/2602.13807/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings. However, most existing methods frame anomaly detection as a purely discriminative prediction task with fixed feature representations, rather than an evidence-driven diagnostic process. As a result, they often struggle when anomalies exhibit strong context dependence, diverse patterns, or domain shifts across datasets. To address these challenges, we propose AnomaMind, an agentic time series anomaly dete","authors_text":"Mingyue Cheng, Tian Gao, Xiaoyu Tao, Yuchong Wu, Ze Guo","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-14T14:35:34Z","title":"AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.13807","kind":"arxiv","version":2},"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":"sha256:6d4a2294543e4ddc1fcb925f888bae143c0d8bcb0a1260b1e3f2cb92be2dd060","target":"record","created_at":"2026-06-10T01:09:56Z","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":"4424ab4dcfbc87387a81d0e9e4087a140aa4e5e42441423e76d98206e77d7fab","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-14T14:35:34Z","title_canon_sha256":"0244569d24ff91c6c5ffe7fb719f8af2d4e8635e10a05e66955e1234ffadd402"},"schema_version":"1.0","source":{"id":"2602.13807","kind":"arxiv","version":2}},"canonical_sha256":"07fbecbb43b63be9cea7140355951125054cc50b5d7c8e5f2fbe3830e9e13bf5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07fbecbb43b63be9cea7140355951125054cc50b5d7c8e5f2fbe3830e9e13bf5","first_computed_at":"2026-06-10T01:09:56.232869Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T01:09:56.232869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wc+ZpQuI+JlWN15zf/cegDYf4l6Ww2QrJN77FvmIJSK1/x32iOoD3qk1zMWjF/yksQtVva5Caan2SUtvd/6cBg==","signature_status":"signed_v1","signed_at":"2026-06-10T01:09:56.234043Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.13807","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d4a2294543e4ddc1fcb925f888bae143c0d8bcb0a1260b1e3f2cb92be2dd060","sha256:4a1a5400d5726b305ca034fb7ee650ace70211d0bcbe8e9bb5982985cf04bf32"],"state_sha256":"7c74f983cb5553c36e5058725b33ac7375b4dd456669b98602868a12b4078075"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wwZKdBHfAVMzHeDfuOpNOg7n+IWZDojv14waD/FaOUbFMeI4+yW+/TE3tgkkTu3xci6NMT0/GPIMjUp7fUjgBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T09:34:34.210781Z","bundle_sha256":"332c4fdd0dd755ac4840c49b35e5118ad4fcb0d3b4fa7d621532d26f3aa07da3"}}