arXiv:2603.03475v1 Announce Type: new Abstract: Mathematical reasoning models are widely deployed in education, automated tutoring, and decision support systems despite exhibiting fundamental computational instabilities. We demonstrate that state-of-the-art models (Qwen2.5-Math-7B) achieve 61% accuracy through a mixture of reliable and unreliable…
arXiv:2603.04353v1 Announce Type: cross Abstract: Next-generation networks aim to provide performance guarantees to real-time interactive services that require timely and cost-efficient packet delivery. In this context, the goal is to reliably deliver packets with strict deadlines imposed by the application…
arXiv:2603.03480v1 Announce Type: new Abstract: We study reinforcement learning with delayed state observation, where the agent observes the current state after some random number of time steps. We propose an algorithm that combines the augmentation method and the upper confidence…
arXiv:2502.01534v3 Announce Type: replace Abstract: Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods in model development. While their combination significantly enhances the efficiency of model training and evaluation, little…
arXiv:2603.03484v1 Announce Type: new Abstract: E-fuels are promising long-term energy carriers supporting the net-zero transition. However, the large combinatorial design-operation spaces under renewable uncertainty make the use of mathematical programming impractical for co-optimizing e-fuel production systems. Here, we present MasCOR,…
arXiv:2507.09768v3 Announce Type: replace Abstract: In recent years, deep learning-based single-channel speech separation has improved considerably, in large part driven by increasingly compute- and parameter-efficient neural network architectures. Most such architectures are, however, designed with a fixed compute and parameter…
arXiv:2603.03491v1 Announce Type: new Abstract: Compute-in-memory (CiM) architectures promise significant improvements in energy efficiency and throughput for deep neural network acceleration by alleviating the von Neumann bottleneck. However, their reliance on emerging non-volatile memory devices introduces device-level non-idealities-such as write…
arXiv:2510.14936v2 Announce Type: replace Abstract: The fields of explainable AI and mechanistic interpretability aim to uncover the internal structure of neural networks, with circuit discovery as a central tool for understanding model computations. Existing approaches, however, rely on manual inspection…
arXiv:2603.03507v1 Announce Type: new Abstract: Adversarial attacks – input perturbations imperceptible to humans that fool neural networks – remain both a persistent failure mode in machine learning, and a phenomenon with mysterious origins. To shed light, we define and analyze…
arXiv:2601.17204v3 Announce Type: replace Abstract: Small-molecule identification from tandem mass spectrometry (MS/MS) remains a bottleneck in untargeted settings where spectral libraries are incomplete. While deep learning offers a solution, current approaches typically fall into two extremes: explicit generative models that…
