This paper introduces Toggle: Token-Granulated Bug Localization and Repair, a comprehensive program that uses LLMs for both bug localization and fixing tasks. The approach is distinct as it precisely predicts bug locations at the token level before employing another LLM for the repair process. Here are the key insights:

• Token-Level Accuracy: Predicts bug locations with granular accuracy.
• Inductive Bias Utilization: Utilizes biases effectively to enhance bug fixing.
• State-of-the-Art Results: Achieves top performance on the CodeXGLUE benchmark.
• Versatile Application: Demonstrable superiority over traditional methods on multiple datasets.

The significance of this study lies in its novel methodology, which separates bug localization from fixing, allowing for more robustness and accuracy. The use of different LLMs to handle distinct phases of bug handling could set a new benchmark in automated programming repairs, proposing potential for comprehensive software maintenance solutions.