{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:L3PWAXUPH3T25WTZETM675BSQB","short_pith_number":"pith:L3PWAXUP","schema_version":"1.0","canonical_sha256":"5edf605e8f3ee7aeda7924d9eff43280539284a21ecfe3e5f1cf047e5c66ecee","source":{"kind":"arxiv","id":"2607.19349","version":1},"attestation_state":"computed","paper":{"title":"FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Mingyuan Wang, Shaoyuan Huang, Tiancheng Zhang, Wenyu Wang, Xiaofei Wang, Yunfeng Zhao","submitted_at":"2026-04-17T13:02:56Z","abstract_excerpt":"Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge. Achieving low latency and high throughput under volatile demand requires deep understanding of real-world serving workloads, yet existing studies often rely on proxy traces or coarse-grained characterizations that fail to capture the heterogeneity of modern multi-model LLM platforms. We present FineServe, an in-the-wild, multi-model LLM serving workload dataset collected from a global commercial marketplace, enabling fine-grained characterization of re"},"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":"2607.19349","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-04-17T13:02:56Z","cross_cats_sorted":[],"title_canon_sha256":"82ce45e72b80f6a56b0bbf74d54c906ce288286c24d55afaf6b1a18579e3e117","abstract_canon_sha256":"0fc5ba1d52d075aa2dde823c31ce64d2530bee97eb5ea0bdf0ef5f40c102efa5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-23T00:23:44.178373Z","signature_b64":"WomnUWCHvLczzHn2wxwsx8itW9eWrnsyOrnH0BVdYc7Xg5g8/21uWCaDbqluUi7EsrAeygE0FQo3d6SbeSKtBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5edf605e8f3ee7aeda7924d9eff43280539284a21ecfe3e5f1cf047e5c66ecee","last_reissued_at":"2026-07-23T00:23:44.177503Z","signature_status":"signed_v1","first_computed_at":"2026-07-23T00:23:44.177503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Mingyuan Wang, Shaoyuan Huang, Tiancheng Zhang, Wenyu Wang, Xiaofei Wang, Yunfeng Zhao","submitted_at":"2026-04-17T13:02:56Z","abstract_excerpt":"Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge. Achieving low latency and high throughput under volatile demand requires deep understanding of real-world serving workloads, yet existing studies often rely on proxy traces or coarse-grained characterizations that fail to capture the heterogeneity of modern multi-model LLM platforms. We present FineServe, an in-the-wild, multi-model LLM serving workload dataset collected from a global commercial marketplace, enabling fine-grained characterization of re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.19349","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/2607.19349/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":"2607.19349","created_at":"2026-07-23T00:23:44.177944+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.19349v1","created_at":"2026-07-23T00:23:44.177944+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.19349","created_at":"2026-07-23T00:23:44.177944+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3PWAXUPH3T2","created_at":"2026-07-23T00:23:44.177944+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3PWAXUPH3T25WTZ","created_at":"2026-07-23T00:23:44.177944+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3PWAXUP","created_at":"2026-07-23T00:23:44.177944+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/L3PWAXUPH3T25WTZETM675BSQB","json":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB.json","graph_json":"https://pith.science/api/pith-number/L3PWAXUPH3T25WTZETM675BSQB/graph.json","events_json":"https://pith.science/api/pith-number/L3PWAXUPH3T25WTZETM675BSQB/events.json","paper":"https://pith.science/paper/L3PWAXUP"},"agent_actions":{"view_html":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB","download_json":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB.json","view_paper":"https://pith.science/paper/L3PWAXUP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.19349&json=true","fetch_graph":"https://pith.science/api/pith-number/L3PWAXUPH3T25WTZETM675BSQB/graph.json","fetch_events":"https://pith.science/api/pith-number/L3PWAXUPH3T25WTZETM675BSQB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB/action/storage_attestation","attest_author":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB/action/author_attestation","sign_citation":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB/action/citation_signature","submit_replication":"https://pith.science/pith/L3PWAXUPH3T25WTZETM675BSQB/action/replication_record"}},"created_at":"2026-07-23T00:23:44.177944+00:00","updated_at":"2026-07-23T00:23:44.177944+00:00"}