High-quality benchmarks are essential for evaluating reasoning and retrieval capabilities of large language models (LLMs). However, curating datasets for this purpose is not a permanent solution as they are prone to data leakage and inflated performance results. To address these challenges, we propose PhantomWiki: a pipeline to generate unique, factually consistent document corpora with diverse question-answer pairs. Unlike prior work, PhantomWiki is neither a fixed dataset, nor is it based on any existing data. Instead, a new PhantomWiki instance is generated on demand for each evaluation. We vary the question difficulty and corpus size to disentangle reasoning and retrieval capabilities, respectively, and find that PhantomWiki datasets are surprisingly challenging for frontier LLMs. Thus, we contribute a scalable and data leakage-resistant framework for disentangled evaluation of reasoning, retrieval, and tool-use abilities.

Health AI suffers from a systemic reproducibility crisis that irreparably hinders research across both academia and industry [4,5].One key tool poised to solve this crisis is the Medical Event Data Standard (MEDS), a comprehensive data format and open-source ecosystem designed to enhance reproducibility and interoperability of AI research using longitudinal Electronic Health Records (EHR) [6].Currently adopted by over 15 institutions globally, MEDS encompasses various open-source tools, published models, and data processing pipelines, enabling streamlined model development and robust benchmarking.In this tutorial, participants will gain key hands-on experience in working with the MEDS format to perform efficient, reproducible, state-of-the-art AI research over real health data.Participants will transform data into the MEDS format, preprocess data, build predictive models, and contribute to the decentralized MEDS-DEV benchmarking platform.Interactive exercises using Jupyter notebooks will provide hands-on experience and practical skills for reproducible health AI research.Attendees will leave equipped

Current approaches for multi-horizon time series forecasting using recurrent neural networks (RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical application domains where an uncertainty estimate is also required. Existing approaches for uncertainty quantification in RNN-based time-series forecasts are limited as they may require significant alterations to the underlying model architecture, may be computationally complex, may be difficult to calibrate, may incur high sample complexity, and may not provide theoretical guarantees on frequentist coverage. In this paper, we extend the inductive conformal prediction framework to the time-series forecasting setup, and propose a lightweight algorithm to address all of the above limitations, providing uncertainty estimates with theoretical guarantees for any multi-horizon forecast predictor and any dataset with minimal exchangeability assumptions. We demonstrate the effectiveness of our approach by comparing it with existing benchmarks on a variety of synthetic and real-world datasets.