Large language models (LLMs) have shown strong performance on medical question answering benchmarks, yet existing evaluations remain limited in assessing longitudinal clinical reasoning across multiple hospital admissions. We introduce ChronoClinQA, a large-scale benchmark for longitudinal clinical reasoning grounded in multi-admission electronic health records. Built from longitudinal discharge summaries in MIMIC-IV-Note, ChronoClinQA contains 15,067 QA pairs spanning six clinically grounded reasoning dimensions and three complementary question formats: open-ended, adversarial, and unanswerable multiple-choice questions. Each instance requires reasoning over temporally accumulated patient trajectories, with contexts averaging over 16K tokens across up to eight hospital admissions. Experiments on both general-purpose and medical LLMs reveal substantial performance degradation in longitudinal and adversarial settings, highlighting persistent challenges in integrating cross-admission evidence and in temporal clinical reasoning. Our findings position ChronoClinQA as a challenging benchmark for advancing clinically grounded long-context reasoning in medical LLMs.

Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated content, leaving the verification of content generated by large language models (LLMs) relatively unexplored. To address this gap, we introduce MedFact, the first evidence-based Chinese medical fact-checking dataset of LLM-generated medical content. It consists of 1,321 questions and 7,409 claims, mirroring the complexities of real-world medical scenarios. We conduct comprehensive experiments in both in-context learning (ICL) and fine-tuning settings, showcasing the capability and challenges of current LLMs on this task, accompanied by an in-depth error analysis to point out key directions for future research. Our dataset is publicly available at https://github.com/AshleyChenNLP/MedFact.

The goal of portfolio management is to simultaneously maximize the accumulated return and also to control risk. In consecutive trading periods, portfolio manager needs to continuously adjust the portfolio weights based on the factors which can cause price fluctuation in the market. In the stock market, the factors affecting the stock price can be divided into two categories. The first is price fluctuations caused by irrational investment of the speculators. The second is endogenous value changes caused by operations of the company. In recent years, with the advancement of artificial intelligence technology, reinforcement learning (RL) algorithms have been increasingly employed by scholars to address financial problems, particularly in the area of portfolio management. However, the deep RL models proposed by these scholars in the past have focused more on analyzing the price changes caused by the investment behavior of speculators in response to technical indicators of actual stock prices. In this research, we introduce an RL-based framework called FinBPM, which takes both the factor pertaining to the impact on operations of the company and the factor of the irrational investment of the speculator into consideration. For our experimentation, we randomly selected twelve stocks from the Dow Jones Industrial Index to construct our portfolio. The experimental results reveal that, in comparison to conventional reinforcement learning methods, our approach with at least 13.26% increase over other methods compared. Additionally, it achieved the best Sharpe ratio of 2.77, effectively maximizing the return per unit of risk.