With the rapid integration of large language models (LLMs) into medical decision-support aids, ensuring reliability in reasoning steps—not just final answers—is increasingly critical. Two key safety dimensions are Chain-of-Thought (CoT) faithfulness, which assesses alignment of the model’s reasoning process with both its response and medical facts, and sycophancy, an emergent misalignment where models follow misleading cues instead of factual correctness. Yet existing benchmarks tend to prioritize performance evaluation, frequently collapsing nuanced safety vulnerabilities into a single accuracy score. To fill this gap, we introduce MedOmni-45°, a benchmark and evaluation workflow explicitly designed to quantify the safety–performance trade-off in LLMs under manipulative hint conditions. The benchmark contains 1,804 reasoning-focused medical questions across six clinical specialties and three task types, including 500 publicly comparable items from MedMCQA. Each question is systematically augmented with seven manipulative hint types, each embedding two distinct misleading cue variants, along with a No-Hint baseline, resulting in approximately 27,000 unique inputs. These inputs are then evaluated across seven LLMs spanning open- and closed-source, general-purpose and medical-specific, and base versus reasoning-enhanced variants, amounting to over 189K total inference instances. Three orthogonal metrics (Accuracy, CoT-Faithfulness, Anti-Sycophancy) are combined into a composite score visualized via a 45° safety–performance plot. Results reveal a universal trade-off, with no model surpassing the ideal diagonal. Open-source QwQ-32B approaches closest at 43.81°, demonstrating notable safety while not surpassing others in performance. MedOmni-45° thus highlights critical vulnerabilities of LLMs in reasoning oriented medical tasks, offering a robust benchmark for future alignment research.

The increasingly popular fused batch-streaming big data framework, Apache Flink, has many performance-critical as well as untamed configuration parameters. However, how to tune them for optimal performance has not yet been explored. Machine learning (ML) has been chosen to tune the configurations for other big data frameworks (e.g., Apache Spark), showing significant performance improvements. However, it needs a long time to collect a large amount of training data by nature. In this article, we propose a guided machine learning (GML) approach to tune the configurations of Flink with significantly shorter time for collecting training data compared to traditional ML approaches. GML innovates two techniques. First, it leverages generative adversarial networks (GANs) to generate a part of training data, reducing the time needed for training data collection. Second, GML guides a ML algorithm to select configurations that the corresponding performance is higher than the average performance of random configurations. We evaluate GML on a lab cluster with 4 servers and a real production cluster in an internet company. The results show that GML significantly outperforms the state-of-the-art, DAC (Datasize-Aware-Configuration) (Z. Yu et al. 2018) for tuning the configurations of Spark, with 2.4× of reduced data collection time but with 30 percent reduced 99th percentile latency. When GML is used in the internet company, it reduces the latency by up to 57.8× compared to the configurations made by the company.