Tool-Augmented Hybrid Ensemble Reasoning with Distillation for Bilingual Mathematical Problem Solving

Bilingual mathematical problem solving needs a clear link between language reasoning and symbolic calculation. Large language models often handle language well but are weak in accurate computation. This paper presents HERALD (Hybrid Ensemble Reasoning with Adaptive Learning and Distillation), a framework that joins reasoning and calculation using NuminaMath-7B-TIR, GPT-4o, and Mistral-7B. HERALD uses adaptive routing, tool-based reinforcement learning, and knowledge distillation to connect different reasoning paths. Confidence calibration keeps weighting stable, and dual-path checking keeps results correct. Reinforcement learning controls tool use to cut redundancy, and distillation lowers delay without hurting accuracy. The system shows that combining symbolic checking, adaptive ensembles, and bilingual fine-tuning helps achieve both fluent reasoning and precise calculation. HERALD offers a practical solution for multilingual mathematical reasoning with better accuracy, stability, and clarity.

A Novel Radar Constant False Alarm Rate Detection Algorithm Based on VAMP Deep Unfolding

The combination of deep unfolding with vector approximate message passing (VAMP) algorithm, results in faster convergence and higher sparse recovery accuracy than traditional compressive sensing approaches. However, deep unfolding alters the parameters in traditional VAMP algorithm, resulting in the unattainable distribution parameter of the recovery error of non-sparse noisy estimation via traditional VAMP, which hinders the utilization of VAMP deep unfolding in constant false alarm rate (CFAR) detection in sub-Nyquist radar system. Based on VAMP deep unfolding, we provide a parameter convergence detector (PCD) to estimate the recovery error distribution parameter and implement CFAR detection. Compared to the state-of-the-art approaches, both the sparse solution and non-sparse noisy estimation are utilized to estimate the distribution parameter and implement CFAR detection in PCD, which leverages both the VAMP distribution property and the improved sparse recovery accuracy provided by deep unfolding. Simulation results indicate that PCD offers improved false alarm rate control performance and higher target detection rate.

Parameter Convergence Detector Based on VAMP Deep Unfolding: A Novel Radar Constant False Alarm Rate Detection Algorithm

The sub-Nyquist radar framework exploits the sparsity of signals, which effectively alleviates the pressure on system storage and transmission bandwidth. Compressed sensing (CS) algorithms, such as the VAMP algorithm, are used for sparse signal processing in the sub-Nyquist radar framework. By combining deep unfolding techniques with VAMP, faster convergence and higher accuracy than traditional CS algorithms are achieved. However, deep unfolding disrupts the parameter constrains in traditional VAMP algorithm, leading to the distribution of non-sparse noisy estimation in VAMP deep unfolding unknown, and its distribution parameter unable to be obtained directly using method of traditional VAMP, which prevents the application of VAMP deep unfolding in radar constant false alarm rate (CFAR) detection. To address this problem, we explore the distribution of the non-sparse noisy estimation and propose a parameter convergence detector (PCD) to achieve CFAR detection based on VAMP deep unfolding. Compared to the state-of-the-art methods, PCD leverages not only the sparse solution, but also the non-sparse noisy estimation, which is used to iteratively estimate the distribution parameter and served as the test statistic in detection process. In this way, the proposed algorithm takes advantage of both the enhanced sparse recovery accuracy from deep unfolding and the distribution property of VAMP, thereby achieving superior CFAR detection performance. Additionally, the PCD requires no information about the power of AWGN in the environment, which is more suitable for practical application. The convergence performance and effectiveness of the proposed PCD are analyzed based on the Banach Fixed-Point Theorem. Numerical simulations and practical data experiments demonstrate that PCD can achieve better false alarm control and target detection performance.

3D-LSPTM: An Automatic Framework with 3D-Large-Scale Pretrained Model for Laryngeal Cancer Detection Using Laryngoscopic Videos

Laryngeal cancer is a malignant disease with a high morality rate in otorhinolaryngology, posing an significant threat to human health. Traditionally larygologists manually visual-inspect laryngeal cancer in laryngoscopic videos, which is quite time-consuming and subjective. In this study, we propose a novel automatic framework via 3D-large-scale pretrained models termed 3D-LSPTM for laryngeal cancer detection. Firstly, we collect 1,109 laryngoscopic videos from the First Affiliated Hospital Sun Yat-sen University with the approval of the Ethics Committee. Then we utilize the 3D-large-scale pretrained models of C3D, TimeSformer, and Video-Swin-Transformer, with the merit of advanced featuring videos, for laryngeal cancer detection with fine-tuning techniques. Extensive experiments show that our proposed 3D-LSPTM can achieve promising performance on the task of laryngeal cancer detection. Particularly, 3D-LSPTM with the backbone of Video-Swin-Transformer can achieve 92.4% accuracy, 95.6% sensitivity, 94.1% precision, and 94.8% F_1.