10 papers
From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement
Qinsi Wang, Jing Shi, Huazheng Wang +8
Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-scale optimization. However, i…
Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
Yudong Liu, Yuan Li, Zijia Tang +12
Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execute at every control step whi…
Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play
Qinsi Wang, Bo Liu, Tianyi Zhou +6
Although reinforcement learning (RL) has emerged as a promising approach for improving vision-language models (VLMs) and multimodal large language models (MLLMs), current methods r…
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Zhongxi Wang, Yueqian Lin, Jingyang Zhang +2
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to systematically test whether alignme…
PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training
Yuhan Cheng, Hancheng Ye, Hai Helen Li +2
Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy violations may arise in agents'…
Bridging the Perception Gap: A Lightweight Coarse-to-Fine Architecture for Edge Audio Systems
Hengfan Zhang, Yueqian Lin, Hai Helen Li +1
Deploying Audio-Language Models (Audio-LLMs) on edge infrastructure exposes a persistent tension between perception depth and computational efficiency. Lightweight local models ten…