Meta multi-stage ads ranking separates long user modeling from online scoring. Learn its scaling laws, transfer ratio, and release gates.
用能力矩阵区分 Agent 安全钩子的可见性、证据质量、激活状态、失败语义与阻断能力。
从参考实现、参数容差、模拟真值、数值诊断到真实数据，建立科学 Agent 的五层验证栈。
A capability matrix for agent security hooks that separates visibility, evidence quality, activation, failure behavior, and enforcement.
A five-layer verification stack for scientific AI agents, from reference parity and parameter tolerances to simulation truth and stewardship.
Meta GEM 将端到端 MFU 提升至 20% 至 25%。真正可复用的是围绕真实负载持续识别并转移瓶颈的方法。
Meta GEM training efficiency shows why LLM-scale recommenders need workload-specific kernels, precision, parallelism, memory, and profiling.
California DROP reaches 600+ data brokers. Use status, matching, deadlines, and follow-up checks to build a verifiable deletion workflow.
产品的 AI 可用性要看 Agent 能否完成真实任务，并通过外部状态、恢复能力、成本与回归稳定性验证。
Test whether AI agents can use your product with dynamic task evals that measure correct state changes, recovery, cost, and regression stability.
