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[2602.20048] CodeCompass: Navigating the Navigation Paradox in Agentic Code Intelligence
Summary
The paper presents CodeCompass, a solution to the Navigation Paradox in code intelligence, highlighting the distinction between navigation and retrieval in coding tasks and demonstrating significant performance improvements through graph-based structural navigation.
Why It Matters
This research addresses a critical gap in code intelligence, where traditional agents struggle with navigation despite vast context. By introducing CodeCompass, it offers a new approach that enhances task completion rates, which is vital for improving coding efficiency and effectiveness in real-world applications.
Key Takeaways
- CodeCompass improves task completion rates by 23.2 percentage points over traditional agents.
- The Navigation Paradox highlights the need for distinct strategies for navigation versus retrieval.
- Graph-based navigation outperforms lexical heuristics, especially in hidden-dependency scenarios.
- A significant adoption gap exists, indicating the need for better behavioral alignment in agents.
- The study contributes a new task taxonomy for understanding code intelligence challenges.
Computer Science > Artificial Intelligence arXiv:2602.20048 (cs) [Submitted on 23 Feb 2026] Title:CodeCompass: Navigating the Navigation Paradox in Agentic Code Intelligence Authors:Tarakanath Paipuru View a PDF of the paper titled CodeCompass: Navigating the Navigation Paradox in Agentic Code Intelligence, by Tarakanath Paipuru View PDF HTML (experimental) Abstract:Modern code intelligence agents operate in contexts exceeding 1 million tokens--far beyond the scale where humans manually locate relevant files. Yet agents consistently fail to discover architecturally critical files when solving real-world coding tasks. We identify the Navigation Paradox: agents perform poorly not due to context limits, but because navigation and retrieval are fundamentally distinct problems. Through 258 automated trials across 30 benchmark tasks on a production FastAPI repository, we demonstrate that graph-based structural navigation via CodeCompass--a Model Context Protocol server exposing dependency graphs--achieves 99.4% task completion on hidden-dependency tasks, a 23.2 percentage-point improvement over vanilla agents (76.2%) and 21.2 points over BM25 retrieval (78.2%).However, we uncover a critical adoption gap: 58% of trials with graph access made zero tool calls, and agents required explicit prompt engineering to adopt the tool consistently. Our findings reveal that the bottleneck is not tool availability but behavioral alignment--agents must be explicitly guided to leverage structura...
