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Abstract:This place paper argues that LLM inference serving has outgrown generic heuristics and now demands mathematical optimization and algorithmic foundations. Despite rapid advances in serving systems such as vLLM and SGLang, their algorithmic cores remain largely unchanged from classical distributed computing: request routing uses join-shortest-queue or spherical-robin, scheduling defaults to FIFO, and KV cache eviction follows LRU. These general-purpose insurance policies ignore the distinctive structure of LLM inference–dynamically growing KV cache memory, prefill-decode part asymmetry, unknown output lengths, and steady batching constraints. We contend that the sphere must develop mathematical fashions capturing these characteristics, enabling the design of algorithms with provable efficiency ensures across numerous workloads, relatively than heuristics that may achieve some scenarios but fail unpredictably in others. Emerging work at the intersection of operations research and ML programs demonstrates that principled methods can match or exceed heuristic efficiency while providing theoretical guarantees. We name on the group to acknowledge algorithmic design for LLM serving as a analysis frontier.