コード空間理論：コードAIエージェントはソフトウェアアーキテクチャを理解できるか？

AI code agents excel at isolated tasks yet struggle with complex, multi-file software engineering requiring understanding of how dozens of modules relate. We hypothesize these failures stem from inability to construct, maintain, and update coherent architectural beliefs during codebase exploration. We introduce Theory of Code Space (ToCS), a benchmark that evaluates this capability by placing agents in procedurally generated codebases under partial observability, requiring them to build structured belief states over module dependencies, cross-cutting invariants, and design intent. The framework features: (1) a procedural codebase generator producing medium-complexity Python projects with four typed edge categories reflecting different discovery methods -- from syntactic imports to config-driven dynamic wiring -- with planted architectural constraints and verified ground truth; (2) a partial observability harness where agents explore under a budget; and (3) periodic belief probing via structured JSON, producing a time-series of architectural understanding. We decompose the Active-Passive Gap from spatial reasoning benchmarks into selection and decision components, and introduce Architectural Constraint Discovery as a code-specific evaluation dimension. Preliminary experiments with four rule-based baselines and five frontier LLM agents from three providers validate discriminative power: methods span a wide performance range (F1 from 0.129 to 0.646), LLM agents discover semantic edge types invisible to all baselines, yet weaker models score below simple heuristics -- revealing that belief externalization, faithfully serializing internal understanding into structured JSON, is itself a non-trivial capability and a first-order confounder in belief-probing benchmarks. Open-source toolkit: https://github.com/che-shr-cat/tocs