Attention Once Is All You Need: Efficient Streaming Inference with Stateful Transformers
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
Researchers propose a fundamental shift in transformer inference architecture, replacing stateless request-driven processing with persistent stateful sessions that maintain incremental KV caches. This eliminates the O(n) prefill penalty on every query, reducing latency to O(|q|) regardless of context depth. The approach enables Flash Queries, a novel pattern where idle GPU cycles pre-compute answers to registered questions before users submit them, a capability impossible in conventional engines. For streaming workloads and real-time systems, this represents a structural efficiency gain that could reshape deployment economics and user experience in production LLM infrastructure.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The deeper implication isn't just latency reduction: persistent stateful sessions fundamentally change the deployment unit from a request to a session, which has real consequences for how inference infrastructure is provisioned, billed, and scaled horizontally across concurrent users.
This week's coverage has leaned heavily toward ML applied to scientific and clinical domains, including the force-aware neural tangent kernel work for molecular simulation and the pregnancy complication prediction study. Neither connects directly to this paper. The relevant prior context lives instead in the ongoing industry conversation about inference cost as the primary constraint on LLM deployment at scale. Stateful Transformers attack that constraint from the architecture side rather than the hardware or quantization side, which is a less-traveled path and worth distinguishing from the compression-focused approaches that dominate most inference optimization coverage.
Watch whether any major inference serving frameworks (vLLM, TensorRT-LLM, SGLang) open issues or RFCs referencing stateful session management within the next three to six months. Adoption signals at that layer would confirm the idea is moving from paper to production consideration.
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MentionsFlash Queries · Stateful Transformers · KV cache
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