Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Current fusion methods are typically evaluated on a small selection of public datasets, a limited scope that inadequately represents the complexity and diversity of real-world scenarios, potentially leading to biased evaluations. This issue presents a twofold challenge. On one hand, models may overfit to the biases of specific datasets, hindering their generalization to broader practical applications. On the other hand, the absence of a unified evaluation standard makes fair and objective comparisons between different fusion methods difficult. Consequently, a truly universal and high-performance fusion model has yet to emerge. To address these challenges, we have developed a large-scale, domain-adaptive benchmark for multimodal evaluation. This benchmark integrates over 30 datasets, encompassing 15 modalities and 20 predictive tasks across key application domains. To complement this, we have also developed an open-source, unified, and automated evaluation pipeline that includes standardized implementations of state-of-the-art models and diverse fusion paradigms. Leveraging this platform, we have conducted large-scale experiments, successfully establishing new performance baselines across multiple tasks. This work provides the academic community with a crucial platform for rigorous and reproducible assessment of multimodal models, aiming to propel the field of multimodal artificial intelligence to new heights.

Advancing biomedical discovery demands creative, well grounded research ideas, yet early stage ideation remains slow, resource-intensive, and uncertain. Recent large language model (LLM) systems show promise for idea generation, but their outputs are rarely evaluated for their potential to meaningfully advance research trajectories. Here we present BioLabAgents, a multi-agent framework that emulates collaborative research for scientific idea generation, where specialized LLM agents iteratively propose, critique, and refine research problems, methods, and experimental designs from a single seed publication. To assess the plausibility and forward-driving value of generated scientific ideas, we introduce the Biomedical Research Trajectory Benchmark (BioRT-Bench), a dataset of temporally ordered paper pairs authored by the same researcher, and propose the Research Trajectory Advancement Index (RTA-Index) to quantify their potential to drive scientific progression. Experiments on BioRTBench show that BioLabAgents produces ideas significantly more aligned with actual future work than baselines. These results highlight the potential of agentic LLM systems to accelerate ideation and forecast impactful directions in biomedical research.