{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KHIPXZVFVFUXSURWX44LGM47PN","short_pith_number":"pith:KHIPXZVF","canonical_record":{"source":{"id":"2510.06063","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-10-07T15:54:34Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"ea52737bb8bd0f9035114c6654a945e4618f5ffecbf09a0dc0b5aa1bd6849d57","abstract_canon_sha256":"09019fb94bec648a817fccbc8ff5dff32bdcc2b5a5ebc5b11e88e88ce4b4ccb2"},"schema_version":"1.0"},"canonical_sha256":"51d0fbe6a5a969795236bf38b3339f7b62ce35973d8166d05f9c09378606ff2e","source":{"kind":"arxiv","id":"2510.06063","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.06063","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"arxiv_version","alias_value":"2510.06063v2","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.06063","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"pith_short_12","alias_value":"KHIPXZVFVFUX","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"pith_short_16","alias_value":"KHIPXZVFVFUXSURW","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"pith_short_8","alias_value":"KHIPXZVF","created_at":"2026-05-21T01:04:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KHIPXZVFVFUXSURWX44LGM47PN","target":"record","payload":{"canonical_record":{"source":{"id":"2510.06063","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-10-07T15:54:34Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"ea52737bb8bd0f9035114c6654a945e4618f5ffecbf09a0dc0b5aa1bd6849d57","abstract_canon_sha256":"09019fb94bec648a817fccbc8ff5dff32bdcc2b5a5ebc5b11e88e88ce4b4ccb2"},"schema_version":"1.0"},"canonical_sha256":"51d0fbe6a5a969795236bf38b3339f7b62ce35973d8166d05f9c09378606ff2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:04:17.557598Z","signature_b64":"hL/+Ouhfy8macwVJBli7WeBdRk9LtQNeMfiBRlAXHuzBfrGVaJ3Hh+HtBFEOdhZlYrxjdGuJHumsqsB6wgajDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51d0fbe6a5a969795236bf38b3339f7b62ce35973d8166d05f9c09378606ff2e","last_reissued_at":"2026-05-21T01:04:17.556644Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:04:17.556644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.06063","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":"2026-05-21T01:04:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"892p76GgpKogKTvGvgthwrsSEKNLiuHFoO9aOugjw+R7QLrd7DCp6J/gy5LzADRGvMHGGL0xIKeuOOJgeqpYDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T04:15:24.890617Z"},"content_sha256":"cb9571fec975f286664e4687b89acfb044281de4ff437f7c6d506fb728be0771","schema_version":"1.0","event_id":"sha256:cb9571fec975f286664e4687b89acfb044281de4ff437f7c6d506fb728be0771"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KHIPXZVFVFUXSURWX44LGM47PN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"cs.AI","authors_text":"Ali Maatouk, Andreas Varvarigos, Austin Feng, Daniela Fernandez, Ioannis Panitsas, Jialin Chen, Jinbiao Wei, Leandros Tassiulas, Rex Ying, Yuwei Guo","submitted_at":"2025-10-07T15:54:34Z","abstract_excerpt":"Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as climate, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets remain underrepresented in public benchmarks due to proprietary restrictions and privacy concerns. Existing datasets are often anonymized and normalized, removing scale information and limiting their use for tasks such as anomaly detection, root cause analysis, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.06063","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/2510.06063/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":"2026-05-21T01:04:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FlAZIucmukVA6XBZojqS1aUnWSKPU2iDGWbrzkVUJyVTesDLBXmdohumYDDRyyaHew+c1J3XN1S5CYsC/s1BAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T04:15:24.891340Z"},"content_sha256":"3d50dd6d20b2df947f12911fad979e9d7f365fe58724834987bb2218e3a30f57","schema_version":"1.0","event_id":"sha256:3d50dd6d20b2df947f12911fad979e9d7f365fe58724834987bb2218e3a30f57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KHIPXZVFVFUXSURWX44LGM47PN/bundle.json","state_url":"https://pith.science/pith/KHIPXZVFVFUXSURWX44LGM47PN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KHIPXZVFVFUXSURWX44LGM47PN/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":"2026-05-25T04:15:24Z","links":{"resolver":"https://pith.science/pith/KHIPXZVFVFUXSURWX44LGM47PN","bundle":"https://pith.science/pith/KHIPXZVFVFUXSURWX44LGM47PN/bundle.json","state":"https://pith.science/pith/KHIPXZVFVFUXSURWX44LGM47PN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KHIPXZVFVFUXSURWX44LGM47PN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KHIPXZVFVFUXSURWX44LGM47PN","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":"09019fb94bec648a817fccbc8ff5dff32bdcc2b5a5ebc5b11e88e88ce4b4ccb2","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-10-07T15:54:34Z","title_canon_sha256":"ea52737bb8bd0f9035114c6654a945e4618f5ffecbf09a0dc0b5aa1bd6849d57"},"schema_version":"1.0","source":{"id":"2510.06063","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.06063","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"arxiv_version","alias_value":"2510.06063v2","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.06063","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"pith_short_12","alias_value":"KHIPXZVFVFUX","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"pith_short_16","alias_value":"KHIPXZVFVFUXSURW","created_at":"2026-05-21T01:04:17Z"},{"alias_kind":"pith_short_8","alias_value":"KHIPXZVF","created_at":"2026-05-21T01:04:17Z"}],"graph_snapshots":[{"event_id":"sha256:3d50dd6d20b2df947f12911fad979e9d7f365fe58724834987bb2218e3a30f57","target":"graph","created_at":"2026-05-21T01:04:17Z","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/2510.06063/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as climate, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets remain underrepresented in public benchmarks due to proprietary restrictions and privacy concerns. Existing datasets are often anonymized and normalized, removing scale information and limiting their use for tasks such as anomaly detection, root cause analysis, and ","authors_text":"Ali Maatouk, Andreas Varvarigos, Austin Feng, Daniela Fernandez, Ioannis Panitsas, Jialin Chen, Jinbiao Wei, Leandros Tassiulas, Rex Ying, Yuwei Guo","cross_cats":["cs.IT","cs.LG","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-10-07T15:54:34Z","title":"TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.06063","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:cb9571fec975f286664e4687b89acfb044281de4ff437f7c6d506fb728be0771","target":"record","created_at":"2026-05-21T01:04:17Z","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":"09019fb94bec648a817fccbc8ff5dff32bdcc2b5a5ebc5b11e88e88ce4b4ccb2","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-10-07T15:54:34Z","title_canon_sha256":"ea52737bb8bd0f9035114c6654a945e4618f5ffecbf09a0dc0b5aa1bd6849d57"},"schema_version":"1.0","source":{"id":"2510.06063","kind":"arxiv","version":2}},"canonical_sha256":"51d0fbe6a5a969795236bf38b3339f7b62ce35973d8166d05f9c09378606ff2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"51d0fbe6a5a969795236bf38b3339f7b62ce35973d8166d05f9c09378606ff2e","first_computed_at":"2026-05-21T01:04:17.556644Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-21T01:04:17.556644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hL/+Ouhfy8macwVJBli7WeBdRk9LtQNeMfiBRlAXHuzBfrGVaJ3Hh+HtBFEOdhZlYrxjdGuJHumsqsB6wgajDA==","signature_status":"signed_v1","signed_at":"2026-05-21T01:04:17.557598Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.06063","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb9571fec975f286664e4687b89acfb044281de4ff437f7c6d506fb728be0771","sha256:3d50dd6d20b2df947f12911fad979e9d7f365fe58724834987bb2218e3a30f57"],"state_sha256":"11104623299f83fc688b636cb9b7ae78181326aacd5557ff988a9b9ad658e7d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X0qVgbTRF+GLhkvFKGbo2xvx+5q4VMzXoqfGObyjeseuZxp4pdNAJwK3csmVQojLZ4hEMorYPEtZQDg8MXDaDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T04:15:24.895160Z","bundle_sha256":"ccbde58b5c586bb56f2dfe1c5e1dd9b49e369bec81a5febfd6ef9ae0a6fb7d18"}}