arXiv:2607. 18253v1 Announce Type: new Abstract: Modern language query routers improve inference efficiency by assigning each query to a model that balances response quality and monetary cost.
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arXiv:2606. 02581v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and end-to-end latency.
TeDiServe is a cluster‑level serving system designed for diffusion language models (DLMs). It addresses DLM‑specific challenges such as the speed‑quality tradeoff from confidence‑based denoising, variable parallelization under fluctuating load, and non‑uniform per‑step costs from approximate KV caching. By employing deadline‑aware scheduling, adaptive load control, and a quality‑aware optimization for cluster reconfiguration, TeDiServe achieves up to 56.6 percentage points higher SLO attainment and reduces end‑to‑end latency by up to 46% with less than 1% accuracy loss.
The paper introduces SaveRouter, a sparse‑supervision framework for large language model routing that selectively gathers informative model feedback and shares capability information across related queries. By using only about 33–41% of available training feedback, SaveRouter achieves competitive or superior routing quality while reducing the break‑even deployment volume by 1.9–9.5× compared to conventional routers. The study also shows that the supervision level that minimizes serving cost may differ from the one that yields the earliest payback.
arXiv:2607. 19349v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge.
arXiv:2508. 06133v4 Announce Type: replace-cross Abstract: We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths.
arXiv:2609.23085v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajector...