为在线压缩服务状态的风险定价,实现可随时验证的准入控制与精准输出约束。
Pricing the Risk of Runtime Compression: Anytime-Valid Admission and a Served-Output Law for Compressed Serving State
- 构建实时可验证的账本机制,确保压缩决策在每一步请求中均可靠。
- 在真实流量上35万次准入调用中,将精确回退率从0.30降至0.14。
- 通过可机器验证的数学定律,明确量化系统误差来源与用户实际体验差距。
运行时压缩服务状态以牺牲精度换取容量,但缺乏风险定价保障:现有系统基于负载信号动态调整精度,无理论保证;已知认证方法通过预设事件数的并集界来预算请求级风险,但在生产环境中所有长请求(100%)都会耗尽该预算。本文提出一种任意时间有效的物理计账机制,在真实流量352,333次准入调用中始终有效,并在预留验证轮次中将精确回退率在相同风险水平下从0.30降至0.14,覆盖范围由账户状态明确定价。随后,本文对认证见证与用户实际体验间的剩余差距进行定价:一个机器验证的设计定律(TV ≤ tanh(a_q w_thr))将目标服务电视值(TV)转化为可调节阈值,三重审计——测量算子范数查询包络(1.5倍紧界)、替代柯西-施瓦茨球的测量椭球体(0.89倍紧界,预留验证有效)、门控操作点(~700倍)——定位出1064倍整体差距源于操作点,此代价由定律明示而非未知。未见请求的随机界毫无价值,因此第三环节引入可交换外推:基于80个服务历史的顺序统计量边界,取代二元共形预测的无效证书,实现区分性校准(校准风险0.41对比0.51)。所有概率核均经Lean 4验证(228条导出定理,无例外),其必要性由配套论文实证确立——驳回了认证路由的自然替代方案。最终交付的是一个可支出的风险账户、可观测的差距读数、以及对未见请求依然成立的可信界。
原文摘要 · Abstract (English)
Runtime compression of serving state trades quality for capacity with no priced guarantee: systems adapt precision on load signals with no soundness statement, and certified approaches budget request-level risk by a union bound over a pre-declared event count. We show the union budget exhausts on every long request in a production serving stack (100% of requests), and replace it with an anytime-valid, physically accounted ledger whose bound holds at every one of 352,333 admission calls on live traffic and which, in a pre-registered held-out confirmatory round, halves the exact-fallback rate at matched risk (0.30 -> 0.14) -- coverage is bought at a price the account states. We then price the remaining distance from the certified witness to what a user experiences: a machine-checked design law (TV <= tanh(a_q w_thr)) turns the served-TV target into a threshold knob, and a three-layer audit of its instantiation -- an operator-norm query envelope measured 1.5x from tight, a measured-ellipsoid replacement for the Cauchy-Schwarz ball that buys nothing (0.89x, held-out sound), and the gate's operating point (~700x) -- localizes the entire 1064x gap to the operating point, a price the law now states rather than an unknown. A priced bound is worth nothing on a request one has not seen, so the third link is the quantifier: exchangeable extrapolation across 80 serving histories replaces binary conformal prediction's vacuous certificates with order-statistic bounds that discriminate (0.41 against 0.51 calibration risk). All probabilistic kernels are Lean 4-checked (228 exported theorems, no sorry); which object deserves this machinery at all is settled empirically in a companion paper that adjudicates -- and rejects -- the natural alternative of certifying routing. What ships is an account: risk you can spend, a gap you can read off a law, and a bound that survives the request you have not seen.
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