2 citations · 2 across the 9 of their papers we have counts for
16 papers
DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models
Shuo Yang, Changbai Li, Linlin Yang +6
Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redundancy or text-vision attention…
GeoNest: Learning to Select Failure-Aware Neighborhoods for the Irregular Knapsack Problem in a Circular Container
Zhongman Du, Huiming Zhang, Linlin Yang +2
The two-dimensional irregular knapsack problem in a fixed circular container is an important combinatorial optimization problem for maximizing material utilization in manufacturing…
Optimal Design for Active Preference Learning with Biased LLM Judges
Zhongman Du, Huiming Zhang, Haodong Zhu +1
Learning from human preferences is central to large language model (LLM) alignment, but human preference annotation is costly. Active preference learning reduces this cost by selec…
GraphHCA: Closed-Form Hindsight Credit Assignment for Long-Horizon LLM Agents
Haodong Zhu, Yangyang Ren, Changbai Li +4
Group-based reinforcement learning (RL) has advanced large language models (LLMs) and is increasingly extending to agentic tasks, where sparse terminal rewards make step-level cred…
Cross-Rollout Bellman Closure for Long-Horizon Agentic Reinforcement Learning
Yangyang Ren, Haodong Zhu, Linlin Yang +3
Group-based reinforcement learning such as GRPO trains LLM agents by comparing rollouts sampled for each task, without a learned critic. In long-horizon settings, these rollouts re…
MaPP: A Unified Marginalized Posterior-Predictive Framework for Data-Efficient RLVR
Yangyang Ren, Haodong Zhu, Sheng Xu +4
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but incurs substantial costs from rollouts and policy updates. On…
Contextual Information Policy Optimization for Search Agents
Xingyu Guo, Wei Chen, Linlin Yang +1
Search agents extend large language models beyond static parametric memory by enabling them to acquire and use external evidence during multi-step reasoning. For knowledge-intensiv…
Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning
Haodong Zhu, Yangyang Ren, Yanjing Li +4
Reinforcement learning (RL) finetuning significantly enhances the reasoning capabilities of large language models (LLMs), yet its effectiveness critically depends on selecting prom…
InfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection
Zichao Feng, Haodong Zhu, Jingying Yang +8
Robust object detection under adverse visual conditions remains a long-standing challenge for multi-modal perception systems. Existing fusion-based methods typically require both R…
Learning domain-invariant features through channel-level sparsification for Out-Of Distribution Generalization
Haoran Pei, Yuguang Yang, Kexin Liu +2
Out-of-Distribution (OOD) generalization has become a primary metric for evaluating image analysis systems. Since deep learning models tend to capture domain-specific context, they…
Causally Guided Gaussian Perturbations for Out-Of-Distribution Generalization in Medical Imaging
Haoran Pei, Yuguang Yang, Kexin Liu +1
Out-of-distribution (OOD) generalization remains a central challenge in deploying deep learning models to real-world scenarios, particularly in domains such as biomedical images, w…
WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection
Haodong Zhu, Wenhao Dong, Linlin Yang +10
Leveraging the complementary characteristics of visible (RGB) and infrared (IR) imagery offers significant potential for improving object detection. In this paper, we propose WaveM…
Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method
Qingcheng Zhu, Yangyang Ren, Linlin Yang +6
Deploying large language models (LLMs) is challenging due to their massive parameters and high computational costs. Ultra low-bit quantization can significantly reduce storage and…
Graph Structure Refinement with Energy-based Contrastive Learning
Xianlin Zeng, Yufeng Wang, Yuqi Sun +3
Graph Neural Networks (GNNs) have recently gained widespread attention as a successful tool for analyzing graph-structured data. However, imperfect graph structure with noisy links…
DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays
Xuhui Liu, Zhi Qiao, Runkun Liu +5
Computed tomography (CT) is widely utilized in clinical settings because it delivers detailed 3D images of the human body. However, performing CT scans is not always feasible due t…
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and Integration
Yuguang Yang, Runtang Guo, Sheng Wu +6
Interpreting complex deep networks, notably pre-trained vision-language models (VLMs), is a formidable challenge. Current Class Activation Map (CAM) methods highlight regions revea…