3 papers
cs.AI2026
ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices
Dezhi Kong, Zhengzhao Feng, Qiliang Liang +12
Multimodal large language models (MLLMs) have made significant progress in mobile agent development, yet their capabilities are predominantly confined to a reactive paradigm, where…
cs.LG2026
Controllable Exploration in Hybrid-Policy RLVR for Multi-Modal Reasoning
Zhuoxu Huang, Mengxi Jia, Hao Sun +2
Reinforcement Learning with verifiable rewards (RLVR) has emerged as a primary learning paradigm for enhancing the reasoning capabilities of multi-modal large language models (MLLM…
cs.CV2025
TeleEgo: Benchmarking Egocentric AI Assistants in the Wild
Jiaqi Yan, Ruilong Ren, Jingren Liu +13
Egocentric AI assistants in real-world settings must process multi-modal inputs (video, audio, text), respond in real time, and retain evolving long-term memory. However, existing…