2 citations · 2 across the 10 of their papers we have counts for
10 papers
Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations
Xianghao Jiao, Ruoyu Chen, Wei Wang +6
Attribution methods are widely used to characterize the evidence underlying model predictions, yet their potential to improve model behavior remains underexplored. Attribution inco…
Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving
Yun Li, Jiachen Gong, Simon Thompson +7
Large language models bring instruction following and scene reasoning to end-to-end driving, but their inference latency collides with the control rate a vehicle requires. Existing…
Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints
Ruoyu Chen, Shangquan Sun, Xiaoqing Guo +8
Reliable models should not only predict correctly, but also base their decisions on acceptable evidence. However, conventional supervised learning typically provides only class-lev…
BeSafe-Bench: Unveiling Behavioral Safety Risks of Situated Agents in Functional Environments
Yuxuan Li, Yi Lin, Peng Wang +2
The rapid evolution of Large Multimodal Models (LMMs) has enabled agents to perform complex digital and physical tasks, yet their deployment as autonomous decision-makers introduce…
Did Models Learn Sufficiently? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation
Yannan Chen, Ruoyu Chen, Wei Wang +6
Current visual models often make predictions based on a limited set of discriminative visual cues. As a result, they may become unreliable when the distribution shifts or when thes…
The Safety Challenge of World Models for Embodied AI Agents: A Review
Lorenzo Baraldi, Zifan Zeng, Chongzhe Zhang +8
The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental…
Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
Ruoyu Chen, Xiaoqing Guo, Kangwei Liu +6
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in aligning visual inputs with natural language outputs. Yet, the extent to which generated token…
Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models
Jiawei Liang, Jianjie Huang, Xianghao Jiao +3
Multimodal large language models (MLLMs) have achieved strong vision-language performance, yet their token-level visual evidence remains difficult to inspect. Recent logit-lens att…
World Models: The Safety Perspective
Zifan Zeng, Chongzhe Zhang, Feng Liu +4
With the proliferation of the Large Language Model (LLM), the concept of World Models (WM) has recently attracted a great deal of attention in the AI research community, especially…
Interpreting Object-level Foundation Models via Visual Precision Search
Ruoyu Chen, Siyuan Liang, Jingzhi Li +5
Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. Howev…