@omarsar0
One Tool is Enough. When building AI agents, more tools often lead to more failure points. This new research introduces RepoNavigator, an LLM agent equipped with a single execution-aware tool: jump. It retrieves the definition of any symbol in a given file, mirroring actual code execution semantics. They find that a single capable tool outperforms multiple narrow-scope tools executed in sequence. RepoNavigator is trained end-to-end via RL directly from pretrained Qwen models, with no closed-source distillation required. The reward combines localization accuracy (Dice score) with tool-calling success rate. On SWE-bench Verified, the 7B model outperforms 14B baselines. The 14B model surpasses 32B competitors. The 32B model exceeds Claude-3.7-Sonnet. The access scope of "jump" is inherently smaller than the full repository. By recursively resolving symbol references from an entry point, the tool traverses only semantically relevant code paths. This constrained search space yields higher precision without sacrificing recall. Ablations confirm that adding tools like GetClass, GetFunc, and GetStruc actually decreases performance. IoU drops from 24.28% with jump alone to 13.71% with all four tools. Tool design for agents should prioritize capability over quantity. One execution-aware tool, jointly optimized with RL, provides stronger robustness than multi-tool pipelines.
