arXiv:2508.18526v2 Announce Type: replace Abstract: A main open question in contemporary AI research is quantifying the forms of reasoning neural networks can perform when perfectly trained. This paper answers this by interpreting reasoning tasks as circuit emulation, where the gates…
arXiv:2509.12346v1 Announce Type: new Abstract: The Engineers’ Salary Prediction Challenge requires classifying salary categories into three classes based on tabular data. The job description is represented as a 300-dimensional word embedding incorporated into the tabular features, drastically increasing dimensionality. Additionally,…
arXiv:2509.11967v2 Announce Type: replace Abstract: Large language models equipped with Web search, information retrieval tools, and other agentic capabilities are beginning to supplant traditional search engines. As users start to rely on LLMs for information on many topics, including controversial…
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…
