FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads

2026-07-23T08:52:13Zb34284d029b6892fd46df4eb3fda7c76f2095a7ef444b41cec44e2f16e60813a
LLM-securityadversarial-mlconfidential-computingdatasetsfraud-detectiongpu-inferenceinformation-discernmentlong-context-attentionmodel-evaluationmulti-agent-systemsruntime-safetyself-play-learningworkload-benchmark

What happened

Collection of new ML/AI systems and benchmarks with multiple security-relevant contributions. Key items: FineServe — a fine-grained, in-the-wild multi-model LLM serving workload dataset and workload generator for realistic routing/scheduling/capacity planning; FraudShield AI — an LSTM+graph-topology hybrid for resilient financial-fraud detection under extreme class imbalance; OpenEvoShield — a co-evolutionary continual-defense framework for adversarial instruction injection in LLM-based multi-agent systems; a benchmark of confidential GPU inference (NVIDIA H100 under Intel TDX) quantifying 11–

Why it matters

A reviewed impact interpretation has not been published for this record.

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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