arXiv:2606. 13241v1 Announce Type: new Abstract: Defining query difficulty is one of the hardest problems in deployment engineering.
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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.
The paper demonstrates that in open‑weight LLM inference markets, selecting a model is insufficient; clients must also choose a provider, as the same model can differ markedly in quality, latency, availability, and price across providers. The authors propose a market‑aware routing approach, including a measured‑map policy and an online router called FACET, which certifies provider feasibility for each task and safely falls back to a reliable anchor. Experiments show that this strategy yields cost savings while maintaining quality and avoiding degraded endpoints.
arXiv:2605. 17106v2 Announce Type: replace-cross Abstract: Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitude cost differences.
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.
arXiv:2603. 20895v3 Announce Type: replace-cross Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task difficulty.
arXiv:2608.28726v1 Announce Type: new Abstract: The remarkable performance of multimodal large language models (MLLMs) comes at the cost of substantial computational overhead, posing significant chal...