Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving

The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests. LLM serving platforms today define response-latency service-level objectives, even though requests within the same service can differ by orders of magnitude in input length, generation length, execution cost, and the availability of reusable KV-cache state. As a result, requests governed by the same service level o

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ethics.ai (6 August 2026), “Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving,” evidence record 17935, https://ethics.ai/record/17935 (originally published by arXiv fairness query).

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