Introduction: Audio as a First-Class Intelligence Modality Audio is one of the most information-dense and perceptually sensitive modalities through which humans interact with the world. Unlike vision or text, audio is continuous in time, governed by physical acoustics, and immediately evaluated by the human auditory system with millisecond-level sensitivity. These properties make audio uniquely challenging for artificial intelligence systems. Even small distortions—phase discontinuities, temporal smearing, and spectral artifacts—can result in degraded intelligibility, listener fatigue, or loss of immersion. […]

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Hi guys, I am a junior data scientist working in the internal audit department of a non banking financial institution. I have been hired for the role of a model risk auditor. Prior to this I have experience only in developing and evaluation logistic probability of default models. Now i audit the model validation team(mrm) at my current company.so i basically am stuck on a issue as there is no one in my team with a technical background, […]

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arXiv:2512.00698v3 Announce Type: replace-cross Abstract: Synthetic data generation is an important tool for privacy-preserving data sharing. Although diffusion models have set recent benchmarks, flow matching (FM) offers a promising alternative. This paper presents different ways to implement FM for tabular data synthesis. We provide a comprehensive empirical study that compares flow matching (FM and variational FM) with a state-of-the-art diffusion method (TabDDPM and TabSyn) in tabular data synthesis. We evaluate both the standard Optimal Transport (OT) and the […]

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arXiv:2509.21091v3 Announce Type: replace Abstract: We study best-of-$N$ for large language models (LLMs) where the selection is based on majority voting. In particular, we analyze the limit $N to infty$, which we denote as boinflower. While this approach achieves impressive performance in the limit, it requires an infinite test-time budget. To address this, we propose an adaptive generation scheme that selects $N$ based on answer agreement, thereby efficiently allocating inference-time computation. Beyond adaptivity, we extend the framework to […]

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arXiv:2603.00597v1 Announce Type: new Abstract: Inertial Odometry (IO) has gained attention in quadrotor applications due to its sole reliance on inertial measurement units (IMUs), attributed to its lightweight design, low cost, and robust performance across diverse environments. However, most existing learning-based inertial odometry systems for quadrotors either use only IMU data or include additional dynamics-related inputs such as thrust, but still lack a principled formulation of the underlying physical model to be learned. This lack of interpretability hampers […]

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Discussions and questions about methods, approaches, and tools for estimating (relative) binding free energies of protein-ligand complexes are quite popular, and even the simplest tools can be quite tricky to use. Here, I want to briefly summarize the idea of molecular docking, and give a short overview about how we can use AutoDock 4.2’s hybrid approach for evaluating binding affinities.

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arXiv:2603.26771v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) generate text by iteratively unmasking tokens from a fully masked sequence, offering parallel generation and bidirectional context. However, their standard confidence-based unmasking strategy systematically defers high-entropy logical connective tokens, the critical branching points in reasoning chains, leading to severely degraded reasoning performance. We introduce LogicDiff, an inference-time method that replaces confidence-based unmasking with logic-role-guided unmasking. A lightweight classification head (4.2M parameters, 0.05% of the base model) predicts the […]

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