Xiaoyang Wu, Jiajun Liu, Haojie Ren, Changliang Zou
Novelty detection is an important area of research in both statistics and machine learning. In this paper, we focus on the conditional novelty detection problem, where novelties arise from the relationship between different variables. We first adopt the conformal inference framework and propose a data-enhanced localized conformal p-value, constructed by recalibrating an enhanced conditional distribution estimator. This estimator efficiently captures conditional information by incorporating both calibration and test data into its kernel estimation. We show that our p-values are valid in finite samples and can improve detection efficiency. Based on this p-value, we then develop a novel conditional novelty detection algorithm, along with a data-driven bandwidth selection method that ensures finite-sample false discovery rate control while enhancing detection power. Both simulated and real data experiments demonstrate the advantages of our proposed approach.
Jiaming Leng, Yunying Bi University of Science and Technology of China, Chuan Qin Chinese Academy of Sciences, Zhenya Huang University of Science and Technology of China, Bing Yin iFLYTEK, Haojie Ren, Yanyong Zhang University of Science and Technology of China, Chao Wang University of Science and Technology of China
Urban transportation systems require precise modeling of dynamic spatiotemporal patterns across diverse tasks, such as traffic forecasting, electric vehicle (EV) charging demand prediction, and taxi dispatch. Existing approaches suffer from two key limitations: traditional deep learning models are task-specific and lack generalization capabilities, whereas Large Language Models (LLMs) struggle with structured spatiotemporal data and numerical reasoning. To bridge this gap, we propose TransLLM, a unified multi-task framework that synergizes spatiotemporal encoding with LLM reasoning through learnable prompt composition. To enable LLMs to perceive complex graph dependencies, we design a noise-augmented spatiotemporal encoder that projects structured signals into the LLM’s embedding space. Furthermore, to overcome the rigidity of fixed prompt templates in heterogeneous traffic scenarios, we introduce an instance-level prompt routing mechanism trained via reinforcement learning. The framework operates by encoding spatiotemporal patterns into contextual representations, dynamically composing personalized prompts to guide LLM reasoning, and projecting the resulting representations through specialized output layers to generate task-specific predictions. Experiments on seven datasets and three tasks demonstrate that TransLLM outperforms many baselines, showing superior adaptability in both supervised and zero-shot settings with excellent generalization and robustness. Our code and data are available at https://github.com/lengjiaming/TransLLM.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表