15 papers · 1 filter
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…
GleanVID: Complementary Token Selection for Efficient Video Large Language Models
Shuo Yang, Changbai Li, Rui Tang +3
Video Large Language Models (VideoLLMs) have achieved strong video understanding capabilities but incur substantial inference overhead due to the large number of visual tokens. Exi…
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…
MHE-Former: Multi-Hypothesis Transformers via Entropy Maximization for 3D Mesh Recovery
Boshu Jia, Rongyu Chen, Linlin Yang +9
Monocular 3D hand and body mesh recovery often suffers from severe occlusion and ambiguity. Traditional deterministic methods typically regress a single optimal solution, leading t…
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…
FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
Haoyu Huang, Linlin Yang, Sheng Xu +5
Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being w…
LDFE: Laplacian Decoupled Feature Enhancement Block for Dual-Stream CNN-based RGB-IR Object Detection
Wenhao Dong, Xiaoyan Luo, Linlin Yang +4
The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions. Existing methods prefer dual-stream CNN bac…
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…
Teaching Vision-Language-Action Models What to See and Where to Look
Yuguang Yang, Canyu Chen, Zhewen Tan +10
Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing VLAs' training relies heavily on text-centric visual q…
CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection
Zihan Liu, Yuguang Yang, Shengjie Su +5
Continual Object Detection (COD) requires a detector to acquire new categories over time while preserving previously learned ones. This goal is closely related to open-vocabulary d…
Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Models
Canyu Chen, Yuguang Yang, Zhewen Tan +10
We identify a fundamental Narrow Policy limitation undermining the performance of autonomous VLA models, where driving Imitation Learning (IL) tends to collapse exploration and lim…
Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization
Haodong Zhu, Yangyang Ren, Yanjing Li +6
Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs due to its extensive group-based sampling requirement. While r…
UrbanGS: A Scalable and Efficient Architecture for Geometrically Accurate Large-Scene Reconstruction
Changbai Li, Haodong Zhu, Hanlin Chen +6
While 3D Gaussian Splatting (3DGS) enables high-quality, real-time rendering for bounded scenes, its extension to large-scale urban environments gives rise to critical challenges i…