We introduce QualityFlow, a dynamic agentic workflow for program synthesis. Given the English description of a programming problem and a set of unit tests, the model's goal is to synthesize the correct program that solves the problem and passes the tests. QualityFlow consists of multiple large language model (LLM) agents that resemble a software development team, including code generation, testing, and self-debugging. Existing program synthesis methods face three major limitations: assumption of visible unit test conformity, bottleneck of synthesized test quality, and deviation of self-debugging trajectory. To address them, we propose the LLM Quality Checker, which explicitly "imagines" whether the synthesized programs' execution would conform to the unit tests. The Quality Checks dynamically control the workflow, including actions to submit the final answer, clarify the problem statement, and revert previous workflow steps. As a result, our Quality Checker can precisely accept any correct program, mitigate faulty synthesized tests, and prevent potential workflow deviation. The success of the Quality Checker further enables Diversified Prompting, which encourages variations in LLM responses to maximize the possibility that a correct program appears and passes the quality check. In experiments, QualityFlow establishes the state-of-the-art results on four program synthesis benchmarks: MBPP, HumanEval, and the stricter evaluations of both MBPP and HumanEval from EvalPlus. Our systematic analysis shows that the dynamic workflow controlled by LLM quality checks can outperform static workflows and single-attempt zero-shot synthesis. The Quality Checker is the center of our investigation, and we dissect its individual performance and integrated impact on the workflow accuracy, as well as other ablations experiments to justify our workflow design.

With the recent popularity of electronic medical records, enormous amount of medical data is being generated every day at an exponential rate.Machine learning methods have been shown in many studies to be capable of producing automatic medical diagnostic models such as automated prognostic models. However, many powerful machine learning algorithms such as support vector machine (SVM), Random Forest (RF) or Kernel Logistic Regression (KLR) are unbearably slow for very large datasets. This makes their use in medical research limited to small to medium scale problems.This study is motivated by an ongoing research on prostate cancer mortality prediction for a national representative of US population where the SVM and RF took several hours or days to trainwhereas simple linear methods such as logistic regression or linear discriminant analysis take minutes or even seconds.Because, most real-world problems are non-linear, this paper presents a large scale algorithm enabling a recently proposed least squares extreme logistic regression to learn very large datasets. The algorithm is shown on a case study of mortality prediction for men diagnosed with early stage prostate cancer to provide very fast and more accurate result than standard statistical methods.