arXiv:2604.05859v1 Announce Type: new
Abstract: We study Contextual Multi-Armed Bandits (CMABs) for non-episodic sequential decision making problems where the context includes both textual and numerical information (e.g., recommendation systems, dynam…
arXiv:2604.05230v1 Announce Type: cross
Abstract: Efficient and robust optimization is essential for neural networks, enabling scientific machine learning models to converge rapidly to very high accuracy — faithfully capturing complex physical behavi…
arXiv:2604.05477v1 Announce Type: new
Abstract: Autonomous GUI agents based on vision-language models (VLMs) often assume deterministic environment responses, generating actions without verifying whether previous operations succeeded. In real-world se…
arXiv:2604.05522v1 Announce Type: new
Abstract: Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal rea…
arXiv:2511.07663v3 Announce Type: replace-cross
Abstract: Snowflake’s Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relatio…
arXiv:2604.05943v1 Announce Type: new
Abstract: Recent advances in multi-agent reinforcement learning (MARL) have demonstrated success in numerous challenging domains and environments, but typically require specialized models for each task. In this wo…
arXiv:2604.05546v1 Announce Type: new
Abstract: Large Vision-Language Models (LVLMs) enable sophisticated reasoning over images and videos, yet their inference is hindered by a systemic efficiency barrier known as visual token dominance. This overhead…
arXiv:2512.23994v2 Announce Type: replace-cross
Abstract: Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to produce physically plausible sounds. Previous bench…
arXiv:2601.05930v2 Announce Type: replace-cross
Abstract: Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer from a severe Execut…
arXiv:2601.05905v2 Announce Type: replace-cross
Abstract: As Large Language Models (LLMs) are increasingly deployed in real-world settings, correctness alone is insufficient. Reliable deployment requires maintaining truthful beliefs under contextual p…