Multimodal Large Language Models (MLLMs) excel in general tasks but struggle with specialized, structured cultural symbols. We introduce BoYaEval, the first comprehensive benchmark dedicated to deciphering diverse Ancient Chinese musical notations, including five types of ancient Chinese music notation systems. These systems utilize unique spatial layouts and specialized ideograms to encode pitch and intricate playing techniques. BoYaE-val comprises 3,175 high-quality images across these notation styles and establishes a three-tier evaluation: Structural Parsing (symbol recognition), Instructional Translation (technique mapping), and Musical Reasoning (melody derivation). We evaluate 21 leading MLLMs. Results indicate that while models perform adequately in basic recognition, they fail in cross-system compositional logic, scoring only around 27% on reasoning tasks. BoYaEval highlights the limitations of current MLLMs in processing diverse spatial-symbolic dependencies, bridging the gap between ancient wisdom and modern AI for digitizing intangible cultural heritage. The BoYaEval benchmark is publicly available at https://huggingface. co
Jiajia Li, Wei-Zhi Xue, Yao Yao et al.· Annual Meeting of the Associ...· 0 citations
Long-video understanding is bottlenecked by the high cost of processing massive visual to-kens. Current reduction strategies often rely on static allocation or inefficient in-network selection that disrupts optimized attention kernels. In this paper, we introduce Vista-LLM, a decoupled framework for query-guided visual token pruning. By filtering redundancy prior to inference with minimal overhead, Vista-LLM ensures full compatibility with Flash Attention. Our method employs a coarse-to-fine pipeline: (1) Query-Guided Dynamic Bud-geting for adaptive temporal allocation; (2) a lightweight Semantic Scout for fine-grained, query-specific selection; and (3) Structure-Aware Compensation to preserve global context. Extensive experiments on benchmarks like Video-MME and MLVU demonstrate a significantly improved Pareto frontier. No-tably, on LLaVA-OneVision, Vista-LLM reduces visual tokens by 90% and accelerates inference while retaining over 98% of baseline performance on average, effectively filtering visual noise. Our code is available at https: //github.com/lizhenyu-123/Vista-LLM .
Zhenyu Li, Zuchao Li, Ping Wang et al.· Annual Meeting of the Associ...· 0 citations