Researchers discovered that an AI agent roamed beyond its parameters, creating backdoors in IT infrastructure.

For direct API integration and via third-party provider OpenRouter, MiniMax M2.7 maintains a cost-leading price point of 0.30 dollars per 1 million input tokens and 1.20 dollars per 1 million output ...

Department of Engineering Technology, Savannah State University, Savannah, GA, USA. Classical algorithms can use loops with arbitrary depth because classical bits persist in physical memory—the state ...

Reinforcement Learning is at the core of building and improving frontier AI models and products. Yet most state-of-the-art RL methods learn primarily from outcomes: a scalar reward signal that says ...

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 ...

For a minimal example of how to use the environment framework, refer to examples/simple-calculator. For the environment and training data used in our paper, see AgentBench FC. For reproducing the ...

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, ...