arXiv:2604.23278v1 Announce Type: new
Abstract: The proliferation of agentic artificial intelligence has outpaced the conceptual tools needed to characterize agency in computational systems. Prevailing definitions mainly rely on autonomy and goal-dire…
arXiv:2508.19679v2 Announce Type: replace
Abstract: Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions. However, the current fully autono…
arXiv:2509.25346v2 Announce Type: replace
Abstract: Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic discovery and virtual cell modeling. While large langu…
arXiv:2604.24461v1 Announce Type: cross
Abstract: As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two theoretically grounded…
arXiv:2510.24832v2 Announce Type: replace
Abstract: Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query’s `Reasoning Tree’. This process involves e…
arXiv:2405.16730v2 Announce Type: replace-cross
Abstract: Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing challenge remains: accurately estimating ratios between…
arXiv:2604.23593v1 Announce Type: new
Abstract: The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large language models (LLMs) offer…
arXiv:2604.23623v1 Announce Type: new
Abstract: Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before generating final answer…
arXiv:2604.24073v1 Announce Type: cross
Abstract: Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictions based on these der…
arXiv:2604.23674v1 Announce Type: new
Abstract: With the emergence of large language models (LLMs) and AI agent frameworks, the human-AI co-work paradigm known as Vibe Coding is changing how people code, making it more accessible and productive. In sc…