3 papers
cs.CV2026
DriveFuture: Future-Aware Latent World Models for Autonomous Driving
Yufeng Hong, Xiaotian Zhou, Yingyan Li +6
Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat future latent states as pre…
cs.AI2026
Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning
Xiaotian Zhou, Di Tang, Xiaofeng Wang +1
Large Language Models (LLMs) have shown a high capability in answering questions on a diverse range of topics. However, these models sometimes produce biased, ideologized or incorr…
cs.CL2025
Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach
Param Kulkarni, Yingchi Liu, Hao-Ming Fu +8
Achieving a delicate balance between fostering trust in law enforcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and prod…