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arXiv:2603.17172v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automated judges and synthetic labelers, especially in low-label settings. Yet these systems …
arXiv:2603.17175v1 Announce Type: new Abstract: Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can …
arXiv:2603.17187v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used for complex tasks, yet deployed agents often remain static, failing to adapt …
arXiv:2603.17196v1 Announce Type: new Abstract: The success of large-scale pretraining in NLP and computer vision has catalyzed growing efforts to develop analogous foundation models for …
arXiv:2603.17198v1 Announce Type: new Abstract: The real world is non-stationary and infinitely complex, requiring intelligent agents to learn continually without the prohibitive cost of retraining …
arXiv:2603.17199v1 Announce Type: new Abstract: Large language models (LLMs) can produce chains of thought (CoT) that do not accurately reflect the actual factors driving their …
arXiv:2603.17246v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit a characteristic "cone effect" in which nonlinear encoders map embeddings into highly concentrated regions of the …
arXiv:2603.17247v1 Announce Type: new Abstract: We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, …
arXiv:2603.17248v1 Announce Type: new Abstract: Reconstructing a 12-lead electrocardiogram (ECG) from a reduced lead set is an ill-posed inverse problem due to anatomical variability. Standard …
arXiv:2603.17255v1 Announce Type: new Abstract: Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for …
arXiv:2603.17278v1 Announce Type: new Abstract: Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based …
arXiv:2603.17301v1 Announce Type: new Abstract: Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using …