arXiv:2512.07877v1 Announce Type: new Abstract: Network-on-Chip (NoC) design requires exploring a high-dimensional configuration space to satisfy stringent throughput requirements and latency constraints.Traditional design space exploration techniques are often slow and struggle to handle complex, non-linear parameter interactions.This work presents a…
arXiv:2502.06909v3 Announce Type: replace Abstract: The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing resources, improving efficiency and scalability. Efficient IIoT…
arXiv:2512.07878v1 Announce Type: new Abstract: Given augmented views of each input graph, contrastive learning methods (e.g., InfoNCE) optimize pairwise alignment of graph embeddings across views while providing no mechanism to control the global structure of the view specific graph-of-graphs built…
MIT researchers identified three cognitive skills that we use to infer what someone really means.
arXiv:2512.07037v2 Announce Type: replace-cross Abstract: Recent image Super-Resolution (SR) models are achieving impressive effects in reconstructing details and delivering visually pleasant outputs. However, the overpowering generative ability can sometimes hallucinate and thus change the image content despite gaining high visual…
arXiv:2409.03505v4 Announce Type: replace-cross Abstract: In the Newsvendor problem, the goal is to guess the number that will be drawn from some distribution, with asymmetric consequences for guessing too high vs. too low. In the data-driven version, the distribution is…
arXiv:2508.02581v2 Announce Type: replace-cross Abstract: Manipulating matter with a scanning tunneling microscope (STM) enables creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM…
arXiv:2509.22462v2 Announce Type: replace Abstract: We propose a reduced-space formulation for optimizing over trained neural networks where the network’s outputs and derivatives are evaluated on a GPU. To do this, we treat the neural network as a “gray box” where…
arXiv:2512.00831v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) are Large Language Models (LLMs) explicitly trained to generate long-form Chain-of-Thoughts (CoTs), achieving impressive success on challenging tasks like math and programming. However, their underlying reasoning “algorithms” remain poorly understood. To…
arXiv:2011.13132v3 Announce Type: replace Abstract: We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymmetric tail dependence between all pairs of individual dimensions while also…
