{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HTAFGJGO757QMH36CV67PGAITL","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":"06683efc83f4ca2571ba8f15e1683d6350fc1e4f9d75e59e7525180a08f370a7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-19T06:15:41Z","title_canon_sha256":"a2cbd3a1643156f7451802190c007da2d0dad2f18ea2ee27c38fed10e57a4dc9"},"schema_version":"1.0","source":{"id":"2606.21130","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.21130","created_at":"2026-06-23T01:12:30Z"},{"alias_kind":"arxiv_version","alias_value":"2606.21130v1","created_at":"2026-06-23T01:12:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21130","created_at":"2026-06-23T01:12:30Z"},{"alias_kind":"pith_short_12","alias_value":"HTAFGJGO757Q","created_at":"2026-06-23T01:12:30Z"},{"alias_kind":"pith_short_16","alias_value":"HTAFGJGO757QMH36","created_at":"2026-06-23T01:12:30Z"},{"alias_kind":"pith_short_8","alias_value":"HTAFGJGO","created_at":"2026-06-23T01:12:30Z"}],"graph_snapshots":[{"event_id":"sha256:3b6b6728df7be92cd11b6f4bf0878555121a715bc3702d55a591095fab498192","target":"graph","created_at":"2026-06-23T01:12:30Z","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/2606.21130/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid growth of large-scale AI workloads, particularly Large Language Model (LLM) training and inference, is fundamentally reshaping the operational dynamics of hyperscale data centers. Unlike traditional cloud workloads, AI-driven jobs exhibit bursty, high-intensity, and rapidly shifting resource demands, often leading to sudden capacity stress that cannot be effectively handled by reactive threshold-based mechanisms. In this paper, we propose a deployment-oriented, burst-aware early warning framework for proactive capacity stress prediction under AI workload surges. We formulate the prob","authors_text":"Sichen Zhao, Xianling Zeng, Yalun Qi, Zhiming Xue, Zihan Yu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-19T06:15:41Z","title":"Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21130","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":"sha256:1f91bd524f886c5ae1e47a31abd78614b2508a7ef8e16bc5471f10fa5ec8728c","target":"record","created_at":"2026-06-23T01:12:30Z","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":"06683efc83f4ca2571ba8f15e1683d6350fc1e4f9d75e59e7525180a08f370a7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-19T06:15:41Z","title_canon_sha256":"a2cbd3a1643156f7451802190c007da2d0dad2f18ea2ee27c38fed10e57a4dc9"},"schema_version":"1.0","source":{"id":"2606.21130","kind":"arxiv","version":1}},"canonical_sha256":"3cc05324ceff7f061f7e157df798089aeace574551ae38d076d5cb8cbf9b309d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3cc05324ceff7f061f7e157df798089aeace574551ae38d076d5cb8cbf9b309d","first_computed_at":"2026-06-23T01:12:30.910969Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T01:12:30.910969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lBNz1wNhg/Q1lMSbu020nuFoiz6crvvTo1Cb2LEOU193IArx4RqPmQGISyukH8QPgb/ZnruyLh1LWO+NjfcOAA==","signature_status":"signed_v1","signed_at":"2026-06-23T01:12:30.911429Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.21130","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f91bd524f886c5ae1e47a31abd78614b2508a7ef8e16bc5471f10fa5ec8728c","sha256:3b6b6728df7be92cd11b6f4bf0878555121a715bc3702d55a591095fab498192"],"state_sha256":"b0ffdcbbd65725179dc92fdb24ba64708a423f115dd4b0375baab0b47504598f"}