Code Understanding is a Bottleneck for Coding Agents
Read the original on arXiv AI →The paper introduces CABRA, a synthetic benchmark that constructs coding tasks from scratch using call‑graph transformations and scales difficulty along four axes: function traversal, search, runtime resolution, and instruction following. Experiments with eight large language models and six coding agents on 6,840 CABRA tasks show that while LLM accuracy degrades with task size, agents maintain high performance by leveraging tools; larger tasks trigger more tool calls, and a separate study on SWE‑bench verifies that tool‑call counts better predict agent accuracy than lines of code edited. The authors argue that CABRA exposes weaknesses in LLMs that are hidden by tool use and highlights the importance of code‑understanding abilities beyond mere editing. whyItMatters":"The study demonstrates that coding agents’ performance is more dependent on code‑understanding than on the amount of code edited, revealing a critical bottleneck for current coding agents."
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