Modern multimodal generative models can synthesize visually compelling images and videos, but it remains unclear whether this visual fluency reflects genuine reasoning: when prompted to generate a solution, can a model preserve the physical, logical, spatial, and temporal constraints a task requires, or does it merely produce plausible-looking media? To answer this question, we introduce MMGR (Multi-Modal Generative Reasoning Benchmark and Evaluation), a benchmark for evaluating generative reasoning across video, image, and language-based systems. MMGR covers 10 tasks from three domains (Abstract Reasoning, Embodied Navigation, and Physical Commonsense) and probes five reasoning abilities: Physical, Logical, 2D Spatial, 3D Spatial, and Temporal. Its evaluation emphasizes answer-verifiable tasks and, for video generation, process-aware chain-of-frame reasoning, where intermediate frames must form valid steps toward the target outcome rather than visually smooth but incorrect transitions. Evaluating state-of-the-art video generators, image generators, and LLM/VLM baselines reveals a sharp gap between visual quality and reasoning correctness: video models perform best on Physical Commonsense, but remain weak on symbolic tasks such as Sudoku, ARC, and Math, and brittle in cross-view embodied navigation. Image generators often outperform video generators on embodied navigation despite lacking temporal outputs, showing that longer visual generation does not automatically yield stronger reasoning. MMGR reframes evaluation of multimodal generation from whether outputs look realistic to whether they solve the underlying reasoning problem.

While language models (LMs) excel at generating code, many programs are difficult to generate using only parametric knowledge. Despite the success of retrieval-augmented generation (RAG) in text-centric tasks, its potential for code generation remains under-explored. This work introduces CodeRAG-bench, a holistic retrieval-augmented code generation benchmark covering tasks like basic programming, open-domain, and repository-level problems and provides reproducible evaluations on both retrieval and end-to-end code generation performance. We further create a diverse, open datastore for code retrieval, aggregating sources such as competition solutions, tutorials, library documentation, StackOverflow posts, and GitHub repositories. Based on CodeRAG-bench, we conduct large-scale evaluations of 10 retrievers and 10 LMs and systematically analyze when retrieval can benefit code generation models and identify remaining challenges. We find that while retrieving high-quality contexts improves code generation, retrievers often struggle to fetch useful contexts, and generators face limitations in using those contexts effectively. We hope CodeRAG-bench encourages further development in code-oriented RAG methods.