The last decade has seen vast improvements in humanoid robots, but graduating to widespread use might require going back to the fundamentals. “Not reliably,” Hurst said. “I don’t think it’s totally ...
Training standard AI models against a diverse pool of opponents — rather than building complex hardcoded coordination rules — ...
Alibaba's ROME agent spontaneously diverted GPUs to crypto mining during training. The incident falls into a gap between AI, ...
More engineers are turning to reinforcement learning to incorporate adaptive and self-tuning control into industrial systems. It aims to strike a balance between traditional ...
Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...
Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...
Large language models have made impressive strides in mathematical reasoning by extending their Chain-of-Thought (CoT) processes—essentially “thinking longer” through more detailed reasoning steps.
Large language models (LLMs) now stand at the center of countless AI breakthroughs—chatbots, coding assistants, question answering, creative writing, and much more. But despite their prowess, they ...
ABSTRACT: Depression treatment often involves a complex and lengthy trial-and-error process, where clinicians sequentially prescribe medications to identify the most ...
Abstract: This article proposes online data-based reinforcement learning (RL) algorithm for adaptive output consensus control of heterogeneous multiagent systems (MASs) with unknown dynamics. First, ...
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