arXiv:2509.20218v1 Announce Type: new Abstract: Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre-recorded datasets, these works often rely on simplified…
arXiv:2509.20215v1 Announce Type: cross Abstract: LLMs face significant challenges in Verilog generation due to limited domain-specific knowledge. While sampling techniques improve pass@k metrics, hardware engineers need one trustworthy solution rather than uncertain candidates. To bridge this gap, we formulate it…
arXiv:2509.20270v1 Announce Type: new Abstract: Managing scan protocols in Computed Tomography (CT), which includes adjusting acquisition parameters or configuring reconstructions, as well as selecting postprocessing tools in a patient-specific manner, is time-consuming and requires clinical as well as technical expertise.…
arXiv:2509.20290v1 Announce Type: cross Abstract: Infectious diseases continue to pose a serious threat to public health, underscoring the urgent need for effective computational approaches to screen novel anti-infective agents. Oligopeptides have emerged as promising candidates in antimicrobial research due to…
arXiv:2509.18122v1 Announce Type: cross Abstract: We introduce textbf{GAUSS} (textbf{G}eneral textbf{A}ssessment of textbf{U}nderlying textbf{S}tructured textbf{S}kills in Mathematics), a benchmark that evaluates LLMs’ mathematical abilities across twelve core skill dimensions, grouped into three domains: knowledge and understanding, problem solving and communication, and…
arXiv:2509.20338v1 Announce Type: cross Abstract: Conventional multi-agent reinforcement learning (MARL) methods rely on time-triggered execution, where agents sample and communicate actions at fixed intervals. This approach is often computationally expensive and communication-intensive. To address this limitation, we propose ET-MAPG (Event-Triggered…
arXiv:2509.19153v1 Announce Type: cross Abstract: Ensuring the correctness of smart contracts is critical, as even subtle flaws can lead to severe financial losses. While bug detection tools able to spot common vulnerability patterns can serve as a first line of…
arXiv:2502.19546v4 Announce Type: replace Abstract: General-purpose vision-language models (VLMs) demonstrate impressive capabilities, but their opaque training on uncurated internet data posse critical limitations for high-stakes decision-making, such as in neurosurgery. We present CNS-Obsidian, a neurosurgical VLM trained on peer-reviewed neurosurgical…
arXiv:2509.19305v1 Announce Type: cross Abstract: Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. However, existing approaches primarily focus on time-domain features while overlooking frequency-domain features, leading to frequency shift and degraded performance…
arXiv:2410.09296v3 Announce Type: replace-cross Abstract: The U.S. Decennial Census serves as the foundation for many high-profile policy decision-making processes, including federal funding allocation and redistricting. In 2020, the Census Bureau adopted differential privacy to protect the confidentiality of individual responses…
