arXiv:2606.26360v1 Announce Type: new Abstract: The neutral, or floating, tone of Mandarin Chinese is a tone with an enigmatic set of properties. It has been described…
arXiv:2504.13432v3 Announce Type: replace Abstract: Imaging through inhomogeneous media often results in severe distortions, posing significant challenges to downstream image-processing tasks. The lack of clean paired…
arXiv:2509.09960v2 Announce Type: replace Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient. Existing…
arXiv:2601.11061v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Qwen 2.5…
arXiv:2505.20178v2 Announce Type: replace Abstract: Prediction-Powered Inference (PPI) is a popular strategy for combining gold-standard and possibly noisy pseudo-labels to perform statistical estimation. Prior work has…
arXiv:2512.19861v2 Announce Type: replace-cross Abstract: Unmeasured confounding can render identification strategies based on adjustment functionals invalid. We study the "Napkin" graph, a causal structure that encapsulates…
arXiv:2410.13077v2 Announce Type: replace-cross Abstract: Transformer-based Large Language Models (LLMs) traditionally rely on final-layer loss for finetuning and final-layer representations for predictions, potentially overlooking the predictive…
arXiv:2606.26300v1 Announce Type: new Abstract: A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being…
arXiv:2606.26108v1 Announce Type: new Abstract: Larger language models consistently outperform smaller ones on reasoning benchmarks, yet the reasoning differences underlying this gap remain underexplored. Across benchmarks…
arXiv:2511.10657v2 Announce Type: replace Abstract: We study self-supervised patent representation learning with contrastive objectives. A standard baseline constructs positives by encoding the same text twice under…
arXiv:2606.26384v1 Announce Type: new Abstract: As deepfake generators approach perceptual indistinguishability, reliable detection becomes critical. Yet, detectors that score well on benchmarks routinely fail in the…
arXiv:2606.26192v1 Announce Type: new Abstract: Hash Learning (HL) is an efficient representation learning approach that maps real-valued data into compact binary representations. Traditional HL methods typically…
arXiv:2606.26257v1 Announce Type: new Abstract: How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this…
arXiv:2209.01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those…
arXiv:2606.26893v1 Announce Type: cross Abstract: We study learning in prophet inequalities with i.i.d. rewards drawn from an exponential-type parametric family with an unknown parameter $theta$, a…
arXiv:2601.16632v4 Announce Type: replace-cross Abstract: Time series forecasting has witnessed significant progress with deep learning. While prevailing approaches enhance forecasting performance by modifying architectures or introducing…
arXiv:2606.26668v1 Announce Type: cross Abstract: Video customization based on Text-to-Video (T2V) models aims to learn specific features from reference data to generate controllable videos. While significant…
arXiv:2606.26105v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong capabilities in short-context reasoning but degrade in performance over long conversational horizons due to context…
arXiv:2606.27277v1 Announce Type: cross Abstract: Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper,…
arXiv:2512.17621v2 Announce Type: replace Abstract: While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide…
arXiv:2606.26991v1 Announce Type: cross Abstract: X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are noisy and incomplete.…
arXiv:2605.20919v3 Announce Type: replace Abstract: Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the…
arXiv:2606.26307v1 Announce Type: cross Abstract: Explainability is increasingly recognized as a key aspect of outlier detection. However, for complex data structures such as interval-valued data, it…
arXiv:2602.18364v3 Announce Type: replace-cross Abstract: Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models. Here, we study a quantum…