BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference

Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Transformer blocks, but existing methods (i) provide limited control under user-specific compute budgets and (ii) typically fix the routing path, failing to adapt as the context grows during decoding. We propose Buddy, a budget-driven dynamic depth routing framework. Buddy uses a lightweight Decision Module to score intermediate layers conditioned on the input and deterministically executes the top-k layers to satisfy a given budget. To support decode-time adaptation, Buddy reuses the first-layer KV cache as a low-overhead global context source and pools it together with the newest token representation before each routing decision. When no explicit budget is provided, an optional Budget Predictor estimates an input-dependent compute level to balance quality and efficiency. Experiments on Llama-family and Qwen models show that Buddy is competitive with strong static pruning baselines and often improves the accuracy-compute trade-off, while uniquely supporting strict budget control, decode-time rerouting, and multiple budgets within a single trained model.

Exploring the Intrinsic Probability Distribution for Hyperspectral Anomaly Detection

In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to the powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background and the anomalies in original data for hyperspectral anomaly detection in this paper. First, we represent the hyperspectral data with multivariate Gaussian distributions from a probabilistic perspective. Then, we combine the local statistics with the obtained distributions to leverage the spatial information. Finally, the difference between the corresponding distributions of the test pixel and the average expectation of the pixels in the Chebyshev neighborhood is measured by computing the modified Wasserstein distance to acquire the detection map. We conduct the experiments on four real data sets to evaluate the performance of our proposed method. Experimental results demonstrate the accuracy and efficiency of our proposed method compared to the state-of-the-art detection methods.