Recent advances in Retrieval-Augmented Generation enable LLMs to ground outputs in clinical evidence, but connections to external databases create the risk of contextual leakage, where unique combinations of medical details enable patient re-identification without explicit identifiers. Existing healthcare benchmarks emphasize accuracy while overlooking this risk. To fill this gap, we present MedPriv-Bench, the first benchmark for jointly evaluating privacy preservation and clinical utility in medical open-ended question answering. Our framework utilizes a multi-agent, human-in-the-loop pipeline to synthesize sensitive medical contexts and clinically relevant queries that create realistic privacy pressure. We also establish an automated evaluation protocol using a fine-tuned RoBERTa-NLI model, which achieved an instance-level F₁-score of 75.3%, sensitivity of 90.7%, and an average inference time of 0.056 s per sample against human annotations. Across nine LLMs and three privacy-preserving methods, we observed a pervasive privacy-utility trade-off. Relative to unprotected Med42-v2-8B (utility 3.87/5; leakage 72.8%), supervised fine-tuning improved utility to 4.25 and reduced leakage to 38.9%, whereas local differential privacy reduced leakage to 20.5% but lowered utility to 3.03. These results demonstrate the need for domain-specific benchmarks to validate medical AI systems in privacy-sensitive settings.

Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains underexplored. Existing approaches are confined to historical backtesting, where trading actions cannot influence market prices, and agents train on static data. To overcome this limitation, we present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive, mult-agent trading and directly impact price dynamics. By simulating realistic bid-ask interactions, our platform enables agents to train in scenarios that closely mirror live markets, thereby narrowing the gap between training and evaluation. Experiments show that LLMs struggle with numerical reasoning when given plain-text data, tending to overfit local patterns and recent values. In contrast, chart-based visualizations significantly boost both numerical reasoning and trading performance. Moreover, integrating a reflection module yields further improvements, especially with visual inputs. Finally, evaluations of the NASDAQ and CSI datasets demonstrate the superiority of our method, particularly under high volatility. All code and data are available at https://github.com/wekjsdvnm/Agent-Trading-Arena.

Vision-language models like CLIP, utilizing class proxies derived from class name text features, have shown a notable capability in zero-shot medical image diagnosis which is vital in scenarios with limited disease databases or labeled samples. However, insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for such paradigm. We show analytically and experimentally that enriching medical texts with detailed descriptions can markedly enhance the diagnosis performance, with the granularity and phrasing of these enhancements having a crucial impact on CLIP’s understanding of medical images; and learning proxies within the vision domain can effectively circumvent the modal gap issue. Based on our analysis, we propose a medical visual proxy learning framework comprising two key components: a text refinement module that create high quality medical text descriptions, and a stable Sinkhorn algorithm for an efficient generation of pseudo labels which further guide the visual proxy learning. Our method elevates the Vanilla CLIP inference by supplying meticulously crafted clues to leverage CLIP’s existing interpretive power and using the feature of refined texts to bridge the vision-text gap. The effectiveness and robustness of our method are clearly demonstrated through extensive experiments. Notably, our method outperforms the state-of-the-art zero-shot medical image diagnosis by a significant margin, ranging from 1.69% to 15.31% on five datasets covering various diseases, confirming its immense potential in zero-shot diagnosis across diverse medical applications.

Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings. Besides, current Med-VQA algorithms, typically reliant on singular models, lack the robustness needed for real-world medical diagnostics which usually require collaborative expert evaluation. To address these shortcomings, this paper presents MedCoT, a novel hierarchical expert verification reasoning chain method designed to enhance interpretability and accuracy in biomedical imaging inquiries. MedCoT is predicated on two principles: The necessity for explicit reasoning paths in Med-VQA and the requirement for multi-expert review to formulate accurate conclusions. The methodology involves an Initial Specialist proposing diagnostic rationales, followed by a Follow-up Specialist who validates these rationales, and finally, a consensus is reached through a vote among a sparse Mixture of Experts within the locally deployed Diagnostic Specialist, which then provides the definitive diagnosis. Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches, providing significant improvements in performance and interpretability.