Recent Large Reasoning Models (LRMs) have achieved remarkable progress, yet their evaluation still relies on a narrow paradigm: evaluating one question at a time. This single-question setup suffers from two major limitations: (1) vulnerability to data contamination and diminishing difficulty, forcing costly creation of new questions with significant human effort, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present **REST** (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST evaluates two under-tested capabilities: *contextual priority allocation* and *robustness against contextual interference*. Our evaluation of more than **30** advanced reasoning models on **9** reasoning benchmarks reveals several striking findings: Even state-of-the-art (SOTA) models such as ***DeepSeek-R1 exhibit substantial performance degradation under stress testing***, challenging the prevailing assumption that "LLMs are multi-problem solvers". Crucially, ***REST demonstrates stronger discriminative power*** than existing benchmarks, revealing performance gaps among models that exhibit similar, near-ceiling performance under traditional evaluation. Some key insights emerge from our analysis: (1) the ***"overthinking trap"*** is a critical factor contributing to the performance degradation; (2) models trained with the ***"Long2Short" technique preserve more of their single-problem accuracy*** under REST, outperforming their standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm while reducing reliance on continuous human annotation. Code is available at https://github.com/opendatalab/REST.

Large Language Models (LLMs) have recently demonstrated remarkable reasoning capabilities across a wide range of tasks. While many benchmarks have been developed on specific academic subjects, coding, or constrained visual tasks, they often fail to fully capture the breadth, diversity, and dynamic nature of real-world human reasoning. Further, the creation of high-quality, complex multimodal reasoning benchmarks typically requires significant manual effort and expert annotation, which is costly and time-consuming.To address these limitations, we introduce Big Escape Bench, a novel multimodal reasoning benchmark derived from popular reality shows and television programs. Big Escape Bench leverages unique characteristics of TV content, providing a rich source of challenging and realistic multimodal reasoning problems. Key advantages include: questions guaranteed to be human-solvable and of moderate difficulty; problems reflecting diverse, real-world scenarios and knowledge domains; high inherent quality due to content generated by professional program teams.Notably, we develop an automated pipeline to construct the data from these programs into a standardized benchmark format, significantly reducing the manual effort compared to traditional dataset construction. We have conducted extensive experiments to evaluate state-of-the-art (SOTA) LLMs and Multimodal Large Language Models (MLLMs) on Big Escape Bench. Our results reveal a surprising performance gap: while the questions are easily solved by human viewers (about 60% in accuracy), the performance of even the most advanced models (best 40.50% in accuracy) is significantly lower than human-level accuracy. This highlights that despite recent progress, MLLMs still face substantial challenges in robustly performing the kind of diverse, dynamic, and context-dependent reasoning that is trivial for humans in routine situations. Big Escape Bench serves as a valuable tool for identifying current limitations of MLLMs and fostering future research towards more human-like multimodal reasoning.