Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects—complexity, readability, and correctness—and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22% accuracy improvement over baseline approaches. Code is available at [Anonymous Github](https://github.com/FireAndWin/ComboPrompt_ExampleQualityMatters).

Step-by-step reasoning is crucial for solving complex visual tasks, yet existing approaches lack a comprehensive framework for evaluating this capability and do not emphasize step-wise problem-solving. To this end, we propose a comprehensive framework for advancing multi-step visual reasoning in large multimodal models (LMMs) through three key contributions. First, we introduce a Visual Reasoning Chain Benchmark, the most comprehensive benchmark for multi-step visual reasoning, covering eight diverse categories and over 4k reasoning steps. This enables rigorous evaluation of LMMs’ ability to reason accurately and interpretably across multiple steps. Second, we propose a fine-grained reasoning metric that evaluates correctness and logical coherence at each step, providing deeper insights beyond traditional accuracy metrics. Third, we introduce LlamaV-o1, a state-of-the-art multimodal reasoning model trained using a multi-step curriculum learning approach. LlamaV-o1 is optimized for structured, step-by-step reasoning and significantly outperforms existing open-source models. It surpasses Llava-CoT with a 3.8% absolute gain across six benchmarks, achieving an average score of 67.3 while being 5x faster during inference scaling. Our benchmark, model, and code is available at https://github.com/mbzuai-oryx/LlamaV-o1.

Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in English language. While few recent works explore multilingual image LMMs, to the best of our knowledge, moving beyond the English language for cultural and linguistic inclusivity is yet to be investigated in the context of video LMMs. In pursuit of more inclusive video LMMs, we introduce a multilingual Video LMM benchmark, named ViMUL-Bench, to evaluate Video LMMs across 14 languages, including both low- and high-resource languages: Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. Our ViMUL-Bench is designed to rigorously test video LMMs across 15 categories including eight culturally diverse categories, ranging from lifestyles and festivals to foods and rituals and from local landmarks to prominent cultural personalities. ViMUL-Bench comprises both open-ended (short and long-form) and multiple-choice questions spanning various video durations (short, medium, and long) with 8k samples that are manually verified by native language speakers. In addition, we also introduce a machine translated multilingual video training set comprising 1.2 million samples and develop a simple multilingual video LMM, named ViMUL, that is shown to provide a better tradeoff between high-and low-resource languages for video understanding. We hope our ViMUL-Bench and multilingual video LMM along with a large-scale multilingual video training set will help ease future research in developing cultural and linguistic inclusive multilingual video LMMs. Our proposed benchmark, video LMM and training data will be publicly released.