arXiv:2509.12358v1 Announce Type: new Abstract: Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions…
arXiv:2410.10596v4 Announce Type: replace-cross Abstract: Since the earliest proposals for artificial neural network (ANN) models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive abilities. Here we review recent work that…
arXiv:2509.12363v1 Announce Type: new Abstract: The agricultural sector is undergoing a transformation with the integration of advanced technologies, particularly in data-driven decision-making. This work proposes a federated learning framework for smart farming, aiming to develop a scalable, efficient, and secure…
arXiv:2506.08171v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated strong performance on coding tasks such as generation, completion and repair, but their ability to handle complex symbolic reasoning over code still remains underexplored. We introduce the task of…
arXiv:2508.06259v4 Announce Type: replace-cross Abstract: Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods have tried to incorporate visual reasoning, however, they fail to leverage attention correction with…
arXiv:2409.13366v4 Announce Type: replace-cross Abstract: Aerial Remote Sensing (ARS) vision tasks present significant challenges due to the unique viewing angle characteristics. Existing research has primarily focused on algorithms for specific tasks, which have limited applicability in a broad range of…
arXiv:2502.12067v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) has been proven effective in enhancing the reasoning capabilities of large language models (LLMs). Recent advancements, such as OpenAI’s o1 and DeepSeek-R1, suggest that scaling up the length of CoT sequences during inference…
arXiv:2509.13227v1 Announce Type: cross Abstract: A rich vehicle routing problem is considered allowing multiple trips of heterogeneous vehicles stationed at distributed vehicle depots spread across diverse geographies having access to different modes of transportation. The problem arises from the real…
arXiv:2509.12434v1 Announce Type: new Abstract: Software engineering presents complex, multi-step challenges for Large Language Models (LLMs), requiring reasoning over large codebases and coordinated tool use. The difficulty of these tasks is exemplified by benchmarks like SWE-bench, where current LLMs still…
arXiv:2509.13021v1 Announce Type: cross Abstract: This work introduces xOffense, an AI-driven, multi-agent penetration testing framework that shifts the process from labor-intensive, expert-driven manual efforts to fully automated, machine-executable workflows capable of scaling seamlessly with computational infrastructure. At its core, xOffense…
