Taming the Uncertainty: AI for Reliable Software
![Model performance varies significantly by task under a guarded configuration ([latex]R_{max}=3[/latex]), with DeepSeek-Coder (1.3B) failing across all tasks, Phi4-Mini demonstrating task-specific reliability (58% LRU, 0% password), and a data corruption issue causing Qwen2.5-Coder (14B) to fail on password tasks.](https://arxiv.org/html/2512.20660v1/model_task_heatmap_guarded.png)
A new formal framework aims to bridge the gap between the probabilistic nature of modern AI and the demand for dependable software systems.
![Model performance varies significantly by task under a guarded configuration ([latex]R_{max}=3[/latex]), with DeepSeek-Coder (1.3B) failing across all tasks, Phi4-Mini demonstrating task-specific reliability (58% LRU, 0% password), and a data corruption issue causing Qwen2.5-Coder (14B) to fail on password tasks.](https://arxiv.org/html/2512.20660v1/model_task_heatmap_guarded.png)
A new formal framework aims to bridge the gap between the probabilistic nature of modern AI and the demand for dependable software systems.

A new runtime system, RHAPSODY, streamlines the execution of complex, hybrid workflows that combine the strengths of high-performance computing and artificial intelligence.

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