arXiv:2601.19568v2 Announce Type: replace Abstract: Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9% redundant invocation rate, which negates parallelism benefits. We propose FuseSearch, reformulating parallel code localization as a joint quality-efficiency optimization} task. Through defining tool efficiency -- the ratio of unique information gain to invocation count -- we utilize a two-phase SFT and RL training approach for learning adaptive parallel stra
The proliferation of more autonomous AI agents necessitates solutions for efficient, parallel task execution to overcome performance bottlenecks and redundant operations.
This research addresses a critical limitation in AI agents' ability to effectively collaborate and execute complex tasks, directly impacting productivity and the scalability of automated software development.
The focus shifts from merely parallel execution to optimizing tool efficiency through joint quality-efficiency objectives, leading to more intelligent and less wasteful agentic systems.
- · AI software development platforms
- · Companies adopting AI for code generation
- · AI agent developers
- · Inefficient AI agent architectures
- · Software development agencies reliant on manual processes
More efficient and faster automated code localization and software development cycles.
Accelerated deployment of new software products and features, increasing competitive pressure on development teams.
A potential reduction in the human oversight needed for routine coding tasks, shifting human roles towards higher-level design and verification.
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Read at arXiv cs.AI