Advanced AI systems can appear helpful while learning behaviors that look deceptive, self-protective, or manipulative.

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If something looks and sounds stupid, but it really works, then it’s not actually stupid! Problems don’t always need to be solved with fancy, expensive, and high-tech approaches. All you really need ...

NVIDIA's GPU-accelerated cuOpt engine discovers new solutions for four MIPLIB benchmark problems, outperforming CPU solvers with 22% lower objective gaps. NVIDIA's cuOpt optimization engine has found ...

A general-purpose Model Context Protocol (MCP) server for solving combinatorial optimization problems with logical and numerical constraints. This server provides a unified interface to multiple ...

Interval-valued optimization problems constitute a rapidly evolving field in applied mathematics and engineering, addressing situations where uncertainty and imprecision are inherent in model ...

Solving optimization problems is challenging for existing digital computers and even for future quantum hardware. The practical importance of diverse problems, from healthcare to financial ...

We present OPT-BENCH, a benchmark comprising 20 machine learning tasks and 10 NP problems, specifically designed to assess large language models’ (LLMs) ability to solve problems with large search ...

Annealing processors (APs) are gaining popularity for solving complex optimization problems. Fully-coupled Ising model APs are especially valued for their flexibility, but balancing capacity (number ...

Impact Statement: The multi-objective traveling salesman problem (MOTSP), one of the typical combinatorial optimization problems, can be used to model a broad range of real applications.

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...