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ARXIV:2605.11232 · LLM SERVING · SUBMITTED 13 MAY · 20:49 UTC · FRESHNESS STALE
ARXIV:2605.11232LLM SERVINGSUBMITTED 13 MAY · 20:49 UTCFRESHNESS STALEPrathamesh Vasudeo Naik · Naresh Dintakurthi · Yue Wang · arXiv
A workload-aware LLMOps stack for fraud and AML compliance, significantly improving throughput and reducing latency for self-hosted open-weight models.
Opportunity summary
Pain A workload-aware LLMOps stack for fraud and AML compliance, significantly improving throughput and reducing latency for self-hosted open-weight models.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A workload-aware LLMOps stack for fraud and AML compliance, significantly improving throughput and reducing latency for self-hosted open-weight models. Compliance prompts are often prefix-heavy, schema-constrained, and evidence-rich, combining reusable policy instructions, risk taxonomies, transaction…
Fraud detection and anti-money-laundering (AML) compliance are high-value domains for large language models (LLMs), but their serving requirements differ sharply from generic chat workloads. Compliance prompts are often prefix-heavy, schema-constrained, and evidence-rich, combining reusable…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. These results show that regulated LLM performance is a workload-design, serving-optimization, and quality-gating problem, not only a model-selection problem. Code availability is flagged in…
LLM Serving moved forward this cycle; last verified May 2026. Public score 9.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score9.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A workload-aware LLMOps stack for fraud and AML compliance, significantly improving throughput and reducing latency for self-hosted open-weight models.
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Paper Pack
10.48550/arXiv.2605.11232A workload-aware LLMOps stack for fraud and AML compliance, significantly improving throughput and reducing latency for self-hosted open-weight models.
Abstract
Fraud detection and anti-money-laundering (AML) compliance are high-value domains for large language models (LLMs), but their serving requirements differ sharply from generic chat workloads. Compliance prompts are often prefix-heavy, schema-constrained, and evidence-rich, combining reusable policy instructions, risk taxonomies, transaction or document context, and short structured outputs such as JSON labels or risk factors. These properties make prefix reuse, KV-cache efficiency, runtime tuning, model orchestration, and output validation first-order systems concerns. This paper introduces a workload-aware LLMOps stack for fraud and AML workloads using self-hosted open-weight models such as Meta Llama and Alibaba Qwen. The stack combines vLLM-style runtime tuning, PagedAttention, Automatic Prefix Caching, multi-adapter serving, adapter and prompt-length-aware batching, sleep/wake lifecycle management, speculative decoding, and optional prefill/decode disaggregation. To avoid exposing institution-specific data, the reproducibility track converts public synthetic AML datasets, including IBM AML and SAML-D, into prefix-heavy compliance prompts with reusable policy text, transaction evidence, typology definitions, and schema-constrained outputs. We also incorporate an LLM-as-judge quality gate using deterministic compliance checks, reference metrics, expert-adjudicated calibration data where available, and multi-judge rubric scoring. Across public-synthetic AML workloads and controlled serving benchmarks, workload-aware tuning improved throughput from 612-650 to 3,600 requests/hour, reduced P99 latency from 31-38 seconds to 6.4-8.7 seconds, and increased GPU utilization from 12% to 78%. These results show that regulated LLM performance is a workload-design, serving-optimization, and quality-gating problem, not only a model-selection problem.
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What was readable
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Dimensions overall score 9.0
PROBLEM
A workload-aware LLMOps stack for fraud and AML compliance, significantly improving throughput and reducing latency for self-hosted open-weight models. Compliance prompts are often prefix-heavy, schema-constrained, and evidence-rich, combining reusable policy instructions, risk...
METHOD
Fraud detection and anti-money-laundering (AML) compliance are high-value domains for large language models (LLMs), but their serving requirements differ sharply from generic chat workloads. Compliance prompts are often prefix-heavy, schema-constrained, and evidence-rich, combin...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. These results show that regulated LLM performance is a workload-design, serving-optimization, and quality-gating problem, not only a model-selection problem. Code availability is flagged in the production...
WHY NOW
LLM Serving moved forward this cycle; last verified May 2026. Public score 9.0/10. Production flags indicate code availability.
increased GPU utilization from 12% to 78%
Directly stated with numeric values in the abstract.
partial
reduced P99 latency from 31-38 seconds to 6.4-8.7 seconds
Directly stated in abstract with specific numeric range and result.
partial
increased GPU utilization from 12% to 78%
Directly stated in abstract with specific numeric values.
partial
workload-aware tuning improved throughput from 612-650 to 3,600 requests/hour
Directly stated in abstract with specific numeric values.
partial
reduced P99 latency from 31-38 seconds to 6.4-8.7 seconds
Directly stated in abstract with specific numeric values.
partial
workload-aware tuning improved throughput from 612-650 to 3,600 requests/hour
Directly stated with numeric evidence in the abstract.
partial
reduced P99 latency from 31-38 seconds to 6.4-8.7 seconds
Directly stated with numeric evidence in the abstract.
partial
increased GPU utilization from 12% to 78%
Directly stated in abstract with specific numeric values.
partial
self-hosted open-weight models such as Meta Llama and Alibaba Qwen
Directly stated in abstract, but no specific model versions mentioned.
partial
The stack combines vLLM-style runtime tuning, PagedAttention, Automatic Prefix Caching, multi-adapter serving, adapter and prompt-length-aware batching, sleep/wake lifecycle management, speculative decoding, and optional prefill/decode disaggregation
Directly stated in abstract as components of the stack.
partial
Compliance prompts are often prefix-heavy, schema-constrained, and evidence-rich
Directly stated in abstract as a characteristic of compliance prompts.
partial
the reproducibility track converts public synthetic AML datasets, including IBM AML and SAML-D, into prefix-heavy compliance prompts
Directly stated in abstract with dataset names.
partial
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Concepts
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A workload-aware LLMOps stack for fraud and AML compliance, significantly improving throughput and reducing latency for self-hosted open-weight models.
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LLM Serving
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9.0/10 public viability
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unverified
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stale
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passport absent
stale
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GitHub and Hugging Face maturity payloads
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Technical feasibility
partial
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Run minimal reproduction from the Build Passport prototype path.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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