arXiv:2509.12146v1 Announce Type: cross Abstract: X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray…
arXiv:2509.10931v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their potential misuse for harmful purposes remains a significant concern. To strengthen defenses against such vulnerabilities, it is essential to investigate universal jailbreak…
arXiv:2412.14222v2 Announce Type: replace Abstract: In recent years, data science agents powered by Large Language Models (LLMs), known as “data agents,” have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution,…
arXiv:2509.10932v1 Announce Type: new Abstract: In-context learning (ICL) in Large Language Models (LLMs) has shown remarkable performance across various tasks without requiring fine-tuning. However, recent studies have highlighted the risk of private data leakage through the prompt in ICL, especially…
arXiv:2410.14827v3 Announce Type: replace-cross Abstract: Prompt injection attack, where an attacker injects a prompt into the original one, aiming to make an Large Language Model (LLM) follow the injected prompt to perform an attacker-chosen task, represent a critical security threat.…
arXiv:2509.10972v1 Announce Type: new Abstract: Computational cognitive architectures are broadly scoped models of the human mind that combine different psychological functionalities (as well as often different computational methods for these different functionalities) into one unified framework. They structure them in…
arXiv:2505.00284v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated significant potential for end-to-end autonomous driving. However, the field still lacks a practical platform that enables dynamic model updates, rapid validation, fair comparison, and intuitive performance assessment. To that end,…
arXiv:2509.11026v1 Announce Type: new Abstract: Large language models (LLMs) often generate natural language rationales — free-form explanations that help improve performance on complex reasoning tasks and enhance interpretability for human users. However, evaluating these rationales remains challenging. While recent work…
arXiv:2507.06813v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. While existing approaches for aggregating client-specific classification heads and adapted backbone parameters require architectural modifications or loss function changes, our method…
arXiv:2509.11035v1 Announce Type: new Abstract: Multi-agent debate (MAD) is an emerging approach to improving the reasoning capabilities of large language models (LLMs). Existing MAD methods rely on multiple rounds of interaction among agents to reach consensus, and the final output…
