TL;DR: KaeTE is presented, an ML-based TE solver that supports dynamic network conditions and the throughput objective and adopts a learning-to-optimize (L2O)-inspired model to iteratively refine the dual variables and ensures that the final TE solutions do not overload any link.
Traffic engineering (TE) is becoming increasingly important in modern networks, as it can improve network performance by splitting traffic across paths. However, traditional TE solvers can be too slow for rapid changes, while recent machine learning (ML) solvers are fast but ofte…
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners. Existing planners usually aggregate heterogeneous scene tokens into a coupled representation space, forcing a single planning branch to jointly model agent i…
Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer. Agent benchmarks test sing…
Self-supervised learning (SSL) pretrained models have become a dominant paradigm for visual representation learning, but they are vulnerable to backdoor attacks. Existing defenses struggle to defend against such attacks in a fully black-box setting because they often require acce…