Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that expose their capability limits. Audio--visual captioning is an ideal diagnostic task, yet current benchmarks face a coupled trade-off: whole-caption scores provide covera...
Zhongyu Yang, Jia-Le Tao, Rui-Tao Chen et al.· 0 citations
A memory-free recent-window protocol is fixed and a transparent and reproducible reference for open-source streaming-video research is provided, combining verifiable streaming-video data, thinking-mode OPD, and instruct-mode deployment.
Keming Wu, Baoyi Wang, Kai-Chen Zhang et al.· 0 citations
This work introduces TOFFEE, a system for synthesizing high-quality data agent trajectories from given data environments via Monte Carlo Tree Search (MCTS) with adaptive model selection and cross-task prefix reuse, and shows that TOFFEE can effectively generate scalable trajectory data for complex analytical tasks acro...
Ziting Wang, Yin Li, Zuhao Yang et al.· arXiv.org· 0 citations
PerceptionBench provides a capability-level standard for measuring and diagnosing the visual perception boundaries of MLLMs, by diagnosing the earliest failure points in the responses of frontier MLLMs across 42 existing benchmarks and constructing an error taxonomy whose perception branch defines ten atomic perceptual...
