FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads
arXiv 2607.19349•b34284d029b6892fd46df4eb3fda7c76f2095a7ef444b41cec44e2f16e60813a
LLM-securityadversarial-mlconfidential-computingdatasetsfraud-detectiongpu-inferenceinformation-discernmentlong-context-attentionmodel-evaluationmulti-agent-systemsruntime-safetyself-play-learningworkload-benchmark
Paper metadata
- arXiv ID
- 2607.19349
- Version
- Not specified by this published record
- Category
- Computer Science — Artificial Intelligence (cs.AI)
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Evidence and limitations
- Source ID
- arxiv_cs_ai
- Record identifier
- b34284d029b6892fd46df4eb3fda7c76f2095a7ef444b41cec44e2f16e60813a
- Enrichment time
- 2026-07-23T08:52:13Z
- AI-assisted enrichment
- Yes
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