15 papers
VLA-: Learning Physical State Transition for Vision-Language-Action Models
Qi Lu, Jiarun Li, Ce Zhang +5
Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performance across various benchmark…
DEEPO: Dual-Entropy Enhanced Policy Optimization for Hallucination in MLLMs
Yingxuan Zhuang, Miao Pan, Wangjie Gan +6
Reinforcement learning (RL) is widely used to sharpen reasoning in multimodal large language models (MLLMs), yet its effect on hallucination is uneven. We trace this to two weak po…
EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks
Lizhou Liang, Xinyu Zhong, Miao Pan +7
Long-horizon embodied interaction requires agents to retain and continually update information about the environment as they observe, act, and encounter change. Yet current agents…
Spatial-Interactor: Learning Spatial Reasoning through Interaction with the Observable Physical World
Kaixiang Yao, Xu Wang, Miao Pan +10
Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local stat…
Embodied-Navigator: Point, Think, Memorize, and Align for Efficient Navigation
Hongyan Feng, Sunlai Chen, Xuanyu Liu +9
Although Large Vision-Language Models (VLMs) have significantly advanced embodied navigation, their direct deployment remains challenging, as existing methods often force VLMs into…
Show, Don't Tell: Evaluating Spatial Cognition in Generative Pixels Rather Than LLM Text
Xu Wang, Kaixiang Yao, Miao Pan +4
Spatial intelligence is essential for agents to move from static semantic understanding toward interacting with the physical world. Many spatial tasks are grounded in continuous vi…
VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
Yi Pan, Miao Pan, Qi Lu +8
Vision-Language-Action (VLA) foundation models have recently achieved strong progress in embodied intelligence. To reduce policy-call frequency while preserving temporal coherence,…
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang +1
Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while r…
Mitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decoding
Yingxuan Zhuang, Jingxiao Yang, Miao Pan +7
MLLMs frequently hallucinate objects inconsistent with visual inputs. This issue is typically attributed to the over-reliance on language priors, which can override the visual cont…
GFT: From Imitation to Reward Fine-Tuning with Unbiased Group Advantages and Dynamic Coefficient Rectification
Wangjie Gan, Miao Pan, Linbo Xi +4
Large language models are typically post-trained using supervised fine-tuning (SFT) and reinforcement learning (RL), yet effectively unifying efficient knowledge injection with rob…
SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition
Jingxiao Yang, DaLin He, Miao Pan +7
Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains ch…
Ground What You See: Hallucination-Resistant MLLMs via Caption Feedback, Diversity-Aware Sampling, and Conflict Regularization
Miao Pan, Wangjie Gan, Jintao Chen +4
While Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse tasks, their practical deployment is severely hindered by hallucination issues, which…
Do Not Merge My Model! Safeguarding Open-Source LLMs Against Unauthorized Model Merging
Qinfeng Li, Miao Pan, Jintao Chen +5
Model merging has emerged as an efficient technique for expanding large language models (LLMs) by integrating specialized expert models. However, it also introduces a new threat: m…
RAGFort: Dual-Path Defense Against Proprietary Knowledge Base Extraction in Retrieval-Augmented Generation
Qinfeng Li, Miao Pan, Ke Xiong +6
Retrieval-Augmented Generation (RAG) systems deployed over proprietary knowledge bases face growing threats from reconstruction attacks that aggregate model responses to replicate…
VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning
Fu Teng, Miao Pan, Xuhong Zhang +6
Recent advancements in code generation have shown remarkable success across software domains, yet hardware description languages (HDLs) such as Verilog remain underexplored due to…