Jiajun Wu

Code large language models have demonstrated remarkable capabilities in programming tasks, yet current benchmarks primarily focus on single modality rather than visual game development. Most existing code-related benchmarks evaluate syntax correctness and execution accuracy, overlooking critical...

Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive labeled (e.g., question-answering pairs) or unlabeled datasets (e.g., code snippets), which are often expensive and di...

While large language models (LLMs) have mastered syntax-level code generation, complex algorithmic reasoning remains a challenge, typically addressed by scaling model depth and parameter count. Universal Transformers (UT) offer a compelling alternative by introducing a recurrent inductive bias th...