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cs.AI2026
Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning
Ke Xu, Yuhao Wang, Ziyang Cheng +3
Multi-hop audio-visual reasoning remains challenging for Omni-LLMs, as relevant evidence is often sparse, temporally dispersed, and distributed across both audio and visual streams…
cs.AI2026
From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench
Ke Xu, Yuhao Wang, Yu Wang
Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existing benchmarks primarily focus…
cs.AI2026
Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models
Shuyang Jiang, Yuhao Wang, Ya Zhang +2
Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts (where all rollouts are correct), resulting…