AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multi-modal planning is promising for autonomous driving by representing multiple plausible behaviors in ambiguous and long-tail scenarios. Existing methods mainly focus on improving trajectory multi-modality, enhancing trajectory representations, or reshaping the candidate distribution. Nevertheless, we identify a pro...
Automated parking commonly assumes marked slots and short approach maneuvers. Delivery and service vehicles, however, may need to reach an operator-specified pose in an irregular bounded environment from a distant start. Existing learning-based parking planners often rely on local observations, which can restrict long-...
On-policy distillation (OPD) has emerged as an effective approach for large language model post-training, yet existing objectives face a trade-off between objective fidelity and optimization stability. Token-level OPD provides stable but local supervision, whereas sequence-level OPD captures future credit at the cost o...
This survey examines RL-based AD in modular and end-to-end pipelines and relates reported methods to task formulation and deployment evidence and examines deployment barriers, including safety, Sim2Real generalization, data efficiency, computation, embodied alignment, and evaluation readiness.
This paper proposes exchange policy optimization (EPO), an algorithmic framework that achieves optimal policy performance with provably bounded safety guarantees and derives an upper bound on the required number of iterations and quantifies the gap between the obtained policy and the true optimum.
An AIM framework based on residual-penalty variable splitting, which interprets momentum as a multiplier-like correction driven by the splitting residual, and RADAR, which combines relativistic adaptive geometry, decoupled residual correction, and second-order momentum filtering to improve the update direction and mome...
A joint identifiability condition for controlled world models with Gaussian latent states with Gaussian latent states is presented, which consists of two coupled components: representation identifiability and transition identifiability, and it is proved that when this condition holds, minimizing the LeJEPA-style predic...
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