Hu

Hammaad Adam, Fan Yin, Huibin et al.

Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account for treatment effect heterogeneity. In this paper, we study the early stopping of experiments for harm on heterogeneous populations. We first establish that current methods often fail to stop experiments when the treatment harms a minority group of participants. We then use causal machine learning to develop CLASH, the first broadly-applicable method for heterogeneous early stopping. We demonstrate CLASH's performance on simulated and real data and show that it yields effective early stopping for both clinical trials and A/B tests.

Gheorghe Comanici, Eric Bieber, Mike Schaekermann et al. · amazon-science, baidu

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.

Ronilo Ragodos, Tong Wang, Lu Feng et al.

Machine learning models have been increasingly used in business research. However, most state-of-the-art machine learning models, such as deep neural networks and XGBoost, are black boxes in nature. Therefore, post hoc explainers that provide explanations for machine learning models by, for example, estimating numerical importance of the input features, have been gaining wide usage. Despite the intended use of post hoc explainers being explaining machine learning models, we found a growing trend in business research where post hoc explanations are used to draw inferences about the data. In this work, we investigate the validity of such use. Specifically, we investigate with extensive experiments whether the explanations obtained by the two most popular post hoc explainers, SHAP and LIME, provide correct information about the true marginal effects of X on Y in the data, which we call data-alignment. We then identify what factors influence the alignment of explanations. Finally, we propose a set of mitigation strategies to improve the data-alignment of explanations and demonstrate their effectiveness with real-world data in an econometric context. In spite of this effort, we nevertheless conclude that it is often not appropriate to infer data insights from post hoc explanations. We articulate appropriate alternative uses, the most important of which is to facilitate the proposition and subsequent empirical investigation of hypotheses. The ultimate goal of this paper is to caution business researchers against translating post hoc explanations of machine learning models into potentially false insights and understanding of data.

Black Sun, Die, Hu

Remote fetal monitoring technologies are becoming increasingly common. Yet, most current systems offer limited interpretability, leaving expectant parents with raw cardiotocography (CTG) data that is difficult to understand. In this work, we present CTG-Insight, a multi-agent LLM system that provides structured interpretations of fetal heart rate (FHR) and uterine contraction (UC) signals. Drawing from established medical guidelines, CTG-Insight decomposes each CTG trace into five medically defined features: baseline, variability, accelerations, decelerations, and sinusoidal pattern, each analyzed by a dedicated agent. A final aggregation agent synthesizes the outputs to deliver a holistic classification of fetal health, accompanied by a natural language explanation. We evaluate CTG-Insight on the NeuroFetalNet Dataset and compare it against deep learning models and the single-agent LLM baseline. Results show that CTG-Insight achieves state-of-the-art accuracy (96.4%) and F1-score (97.8%) while producing transparent and interpretable outputs. This work contributes an interpretable and extensible CTG analysis framework.