Theory of Code Space: Do Code Agents Understand Software Architecture?
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912973449068544 |
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| author | Sapunov, Grigory |
| author_facet | Sapunov, Grigory |
| contents | AI code agents excel at isolated tasks yet struggle with multi-file software engineering requiring architectural understanding. We introduce Theory of Code Space (ToCS), a benchmark that evaluates whether agents can construct, maintain, and update coherent architectural beliefs during codebase exploration. Agents explore procedurally generated codebases under partial observability -- opening files under a budget -- and periodically externalize their belief state as structured JSON, producing a time-series of architectural understanding. Three findings emerge from experiments with four baselines and six frontier LLMs. First, the Active-Passive Gap is model-dependent: one model builds better maps through active exploration than from seeing all files at once, while another shows the opposite -- revealing that active exploration is itself a non-trivial capability absent from some models. Second, retaining structured belief maps in context acts as self-scaffolding for some models but not others, showing that the mechanism is model-dependent. Third, belief state maintenance varies dramatically: a smaller model maintains perfectly stable beliefs across probes while its larger sibling suffers catastrophic belief collapse -- forgetting previously-discovered components between probes. We release ToCS as open-source software. Code: https://github.com/che-shr-cat/tocs |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00601 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Theory of Code Space: Do Code Agents Understand Software Architecture? Sapunov, Grigory Software Engineering Artificial Intelligence AI code agents excel at isolated tasks yet struggle with multi-file software engineering requiring architectural understanding. We introduce Theory of Code Space (ToCS), a benchmark that evaluates whether agents can construct, maintain, and update coherent architectural beliefs during codebase exploration. Agents explore procedurally generated codebases under partial observability -- opening files under a budget -- and periodically externalize their belief state as structured JSON, producing a time-series of architectural understanding. Three findings emerge from experiments with four baselines and six frontier LLMs. First, the Active-Passive Gap is model-dependent: one model builds better maps through active exploration than from seeing all files at once, while another shows the opposite -- revealing that active exploration is itself a non-trivial capability absent from some models. Second, retaining structured belief maps in context acts as self-scaffolding for some models but not others, showing that the mechanism is model-dependent. Third, belief state maintenance varies dramatically: a smaller model maintains perfectly stable beliefs across probes while its larger sibling suffers catastrophic belief collapse -- forgetting previously-discovered components between probes. We release ToCS as open-source software. Code: https://github.com/che-shr-cat/tocs |
| title | Theory of Code Space: Do Code Agents Understand Software Architecture? |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2603.00601 |