CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers

ArXi:2605.21915v1 Announce Type: cross Congestion controllers (CCs) are critical to network performance, and yet their robustness under adverse conditions remains insufficiently understood. While recent learning-based CCs have nstrated strong performance in controlled environments, it is unclear how they compare to traditional CCs when controllers' input signals are corrupted or when environmental conditions become systematically challenging. In this paper, we