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arXiv:2604.14440v1 Announce Type: new Abstract: We propose a Reinforcement Learning (RL) based control design framework for handling complex tasks. The approach

arXiv:2604.14455v1 Announce Type: new Abstract: AI models underpin modern intelligent systems, driving advances across science, medicine, finance, and technolog

arXiv:2604.14465v1 Announce Type: new Abstract: AI systems are increasingly used to assist humans in sequential decision-making tasks, yet determining when and

arXiv:2604.14473v1 Announce Type: new Abstract: A common approach to personalization in large language models (LLMs) is to incorporate a subset of the user memo

arXiv:2604.14475v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents can orchestrate specialist classifiers, segmentation models, an

arXiv:2604.14477v1 Announce Type: new Abstract: Transparency of neural networks' internal reasoning is at the heart of interpretability research, adding to trus

arXiv:2604.14493v1 Announce Type: new Abstract: Deploying high-quality automatic speech recognition (ASR) on edge devices requires models that jointly optimize

arXiv:2604.14498v1 Announce Type: new Abstract: Synthetic augmentation is increasingly used to mitigate data scarcity in financial machine learning, yet its sta

arXiv:2604.14500v1 Announce Type: new Abstract: Expert specialization is fundamental to Mixture-of-Experts (MoE) model success, yet existing metrics (cosine sim

arXiv:2604.14514v1 Announce Type: new Abstract: Healthcare disparities persist across socioeconomic boundaries, often attributed to unequal access to screening,

arXiv:2604.14518v1 Announce Type: new Abstract: We present \textbf{Mind DeepResearch (MindDR)}, an efficient multi-agent deep research framework that achieves l

arXiv:2604.14525v1 Announce Type: new Abstract: Large language models frequently produce mutually inconsistent answers when reasoning over multiple related quer

arXiv:2604.14528v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance through extended inference-time deliberation, yet how th

arXiv:2604.14531v1 Announce Type: new Abstract: Every call to an LLM classification endpoint produces a labeled input-output pair already retained in production

arXiv:2604.14564v1 Announce Type: new Abstract: Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning-intensive tasks such as

arXiv:2604.14576v1 Announce Type: new Abstract: Large language models (LLMs) show promise in generating supportive responses for mental health and counseling ap

arXiv:2604.14585v1 Announce Type: new Abstract: Prompt optimization in compound AI systems is statistically indistinguishable from a coin flip: across 72 optimi

arXiv:2604.14607v1 Announce Type: new Abstract: We study the overall process of automatic formalization of GDPR provisions using large language models, within a

arXiv:2604.14609v1 Announce Type: new Abstract: AI for science promises to accelerate the discovery process. The advent of large language models (LLMs) and agen

arXiv:2604.14615v1 Announce Type: new Abstract: Scientific discovery in digital health requires converting continuous physiological signals from wearable device

arXiv:2604.14627v1 Announce Type: new Abstract: The exact cover problem is a classical NP-hard problem with broad applications in the area of AI. Algorithm DXZ

arXiv:2604.14641v1 Announce Type: new Abstract: When faced with complex spatial problems, humans naturally sketch layouts to organize their thinking, and the ac

arXiv:2604.14646v1 Announce Type: new Abstract: Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models

arXiv:2604.14655v1 Announce Type: new Abstract: We present AgentGA, a framework that evolves autonomous code-generation runs by optimizing the agent seed: the t