Comprehensive software documentation is crucial yet costly to produce. Despite recent advances in large language models (LLMs), generating holistic, architecture-aware documentation at the repository level remains challenging due to complex and evolving codebases that exceed LLM context limits. Existing automated methods struggle to capture rich semantic dependencies and architectural structure. We present CodeWiki, a unified framework for automated repository-level documentation across seven mainstream programming languages. CodeWiki combines top-down hierarchical decomposition with a divide-and-conquer agent system to preserve architectural context and scale documentation generation, and a bottom-up synthesis that integrates textual descriptions with visual artifacts such as architecture and data-flow diagrams. We also introduce CodeWikiBench, a benchmark with hierarchical rubrics and LLM-based evaluation protocols. Experiments show that CodeWiki achieves a 68.79% quality score with proprietary models, outperforming the closed-source DeepWiki baseline by 4.73%, with especially strong gains on scripting languages. CodeWiki is released as open source to support future research.

Large Language Models (LLMs) are increasingly being used to automate programming tasks. However, the capabilities of LLMs in reasoning about program semantics are still inadequately studied, leaving substantial potential for further exploration. This paper introduces FormalBench, a comprehensive benchmark designed to evaluate the reasoning abilities of Large Language Models (LLMs) on program semantics. Specifically, it utilizes the task of synthesizing formal program specifications as a proxy measure for assessing the semantic reasoning of LLMs. This task requires both comprehensive reasoning over all possible program executions and the generation of precise, syntactically correct expressions that adhere to formal syntax and semantics. Using this benchmark, we evaluated the ability of LLMs to synthesize consistent and complete specifications. Our findings show that LLMs perform well with simple control flows but struggle with more complex structures, especially loops, even with advanced prompting. Additionally, LLMs exhibit limited robustness against semantic-preserving transformations. We also highlight common failure patterns and design self-repair prompts, improving success rates by 25%. FormalBench is packaged as an executable library and has been released at https://github.com/thanhlecongg/FormalBench/.