Branch-and-bound (B&B) is a fundamental algorithmic framework for solving Mixed-Integer Linear Programming (MILP) problems, where branching decisions critically affect solver efficiency. Recent learning-based methods apply imitation learning to select branching variables, but their deterministic predictions limit exploration and generalization. In this paper, we propose a novel framework that formulates branching variable selection as a conditional generative process, exploring deep-level decision features. Our approach leverages diffusion models to enable diverse and exploratory branching score generation, while consistency modeling distills this process into efficient one-step inference conditioned on the B&B state. This mode allows our method to achieve both high-quality and fast branching decisions, significantly improving the overall performance of branch-and-bound solvers. Extensive experiments on challenging cross-scale and cross-category benchmarks demonstrate that our framework consistently outperforms state-of-the-art imitation learning baselines, delivering substantial improvements in solution quality, computational efficiency, and inference speed.

Adaptive gradient methods, primarily based on Adam, are prevalent in training neural networks, adjusting step sizes via exponentially decaying averages of gradients and squared gradients. Adam assigns small weights to distant gradients, termed long-tail gradients in this paper. However, these gradients persistently influence update behavior, potentially degrading generalization performance. To address this issue, we incorporate a restart mechanism into moment estimations, proposing AdaR (ADAptive gradient methods via Restarting moment estimations). Specifically, AdaR divides a training epoch into fixed-iteration intervals, alternating between two sets of moment estimations for parameter updates and discarding prior moment estimations at the beginning of each interval. Within each interval, one set updates parameters and will be discarded in the subsequent interval, while the other is reset at the midpoint to estimate moments for updates in the subsequent interval. The restart mechanism cyclically discards distant gradients, initiates fresh moment estimations for parameter updates, and stabilizes training. By prioritizing recent gradients, the method increases estimation accuracy and enhances step size adjustment. Empirically, AdaR outperforms state-of-the-art optimization algorithms on image classification and language modeling tasks, demonstrating superior generalization and faster convergence.