发表机构
The University of Electro-Communications（电子通信大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对自适应比特率流中神经策略与规则结合问题，提出神经符号流形对齐（NSMA）方法，将规则决策嵌入神经策略潜在空间，经实验验证该方法在多数据集及真实播放器上表现优异，优于现有基线。
AI 中文摘要
几十年来，自适应比特率（ABR）一直将两种智能分开。神经策略学习丰富行为，但环境变化时就会忘记；规则从不学习也从不忘记。以往将它们结合的尝试都保持了这种分离，让规则从外部监督、约束或覆盖网络。我们消除了这种界限。但在测试之前，任何结合都不可信，而ABR一直不知如何衡量其策略学到或忘记了什么。该领域的衡量标准是带宽统计，我们表明它会产生误导。相同统计可能隐藏完全不同的结果，而差异很大的统计可能隐藏相似结果。我们在构建桥梁之前更换了衡量标准，采用纹理感知泛化评估协议，通过策略在整个训练过程中的表现来评判。真正破坏策略的因素是无形的。没有统计数据能揭示它，没有特征能提取它，但规则能不受影响地通过，因为规则基于物理原理推理且不依赖数据。所以我们构建了神经符号流形对齐（NSMA），将规则决策作为锚嵌入神经策略的潜在空间，使其能持续学习有价值的内容，不会忘记规则一直知道的东西。我们仅在3G轨迹上训练NSMA并发布，无需微调就将其应用于跨越4G、5G和WiFi的八个未见数据集以及真实播放器上。它优于所有现有基线。当我们打开其潜在空间询问原因时，探测和可视化得到的答案与设计预期一致。
英文摘要
For decades, ABR has kept two kinds of intelligence apart. Neural policies learn rich behaviors yet forget them the moment the environment changes; rules never learn, and never forget. Every prior attempt to combine them has kept this separation, letting rules supervise, constrain, or override the network from outside. We dissolve the boundary itself. But no union can be trusted before it can be tested, and ABR has never known how to measure what its policies learn or forget. The field's yardstick is bandwidth statistics, and we show it misleads. Identical statistics can hide entirely different outcomes, while wildly different statistics can hide similar ones. We replace the yardstick before building the bridge, with Texture-Aware Generalization Evaluation, a protocol that judges a policy by its whole training journey across traces whose temporal nature is laid bare. What truly breaks a policy is invisible. No statistic reveals it, no feature extracts it, yet rules walk through it untouched, for they reason from physics and owe the data nothing. So we build the bridge. Neuro-Symbolic Manifold Alignment (NSMA) embeds rule decisions as anchors inside the latent space of the neural policy, so that it keeps learning where learning pays, and can no longer forget what rules have always known. Generalization cannot be argued, only survived. We raise NSMA on 3G traces alone and release it, without fine-tuning, into eight unseen datasets spanning 4G, 5G, and WiFi, and onto a real-world player. It outperforms every state-of-the-art baseline. And when we open its latent space to ask why, probing and visualization return the same answer the design promised. https://tinyzqh.github.io/NSMA/
