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 bilingual AI audiologist built through rubric-guided closed-loop engineering outperforms human audiologists in a blinded evaluation of simulated cases
Stanford University（斯坦福大学）；Orka Labs Inc.（奥卡实验室公司）；The University of Hong Kong（香港大学）
Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization
Tianjin University（天津大学）；Beijing Institute of Technology（北京理工大学）；Singapore Management University（新加坡国立大学）；University of Chinese Academy of Sciences（中国科学院大学）；Xiaomi Inc（小米公司）
Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models
Hong Kong Baptist University（香港 Baptist 大学）；Shanxi University（山西大学）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海先进研究院）；Hong Kong University of Science and Technology（香港科技大学）；The University of Manchester（曼彻斯特大学）
OPDSearch+: On-Policy Distillation with RL Refinement for Search-Augmented Reasoning
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Macau（澳门大学）；Wuhan University（武汉大学）；Georgia Institute of Technology（佐治亚理工学院）；Northwestern Polytechnical University（西北工业大学）；University of Hong Kong（香港大学）
Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field. Though deep learning methods have achieved promising results, there are still many limitations, for example, how to extract clean features from the raw stock data. In this paper, we introduce an \emph{Augmented Disentanglement Distillation (ADD)} approach to remove interferential features from the noised raw data. Specifically, we present 1) a disentanglement structure to separate excess and market information from the stock data to avoid the two factors disturbing each ot…
Image-based object pose estimation sounds amazing because in real applications the shape of object is oftentimes not available or not easy to take like photos. Although it is an advantage to some extent, un-explored shape information in 3D vision learning problem looks like "flaws in jade". In this paper, we deal with the problem in a reasonable new setting, namely 3D shape is exploited in the training process, and the testing is still purely image-based. We enhance the performance of image-based methods for category-agnostic object pose estimation by exploiting 3D knowledge learned by a multi…
PACD-Net: Pseudo-Augmented Contrastive Distillation for Glycemic Control Estimation from SMBG
University of Virginia, School of Data Science, Charlottesville, VA 22903, USA（弗吉尼亚大学数据科学学院）；University of Virginia, Department of Pediatrics, Charlottesville, VA 22903, USA（弗吉尼亚大学儿科系）
Improving Consistency Models with Generator-Augmented Flows
Criteo AI Lab, Paris, France（Criteo AI实验室，法国巴黎）；Reasoning (AI/R) Laboratory, Korea Institute of Science（推理（AI/R）实验室，韩国科学技术院）；Robot Department, University of Science（机器人部门，科学大学）
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion a…
