An experiment in which AI models were asked to break into simulated networks showed the hacking skills of DeepSeek and GLM to be not far behind OpenAI's most advanced cyber model, which isn't public. Open-weight artificial intelligence models are now almost as capable of hacking into computer networks as the best closed ones, according to new research that heralds a much-anticipated turning point in the cybersecurity landscape. Vals, a company that measures AI capabilities, set out to test how well different AI models could carry out complex, multi-step network instructions on mock corporate networks. "The best frontier models, locked up inside the labs and available only through trusted access programs, are now starting to be matched by open models that anybody can download," says Nikil Ravi, a researcher at Vals. The company's findings suggest that closed models no longer have a definitive edge in cyber skills, meaning recent efforts by frontier labs to limit access to their models, and thereby slow the proliferation of such capabilities, may potentially be moot.

Optimization modeling is one of the most crucial but technical parts of operations research (OR). To automate the modeling process, existing works have leveraged large language models (LLMs), prompting them to break down tasks into steps for generating variables, constraints, and objectives. However, due to the highly complex mathematical structures inherent in OR problems, standard fixed-step decomposition often fails to achieve high performance. To address this challenge, we introduce OptiTree, a novel tree search approach designed to enhance modeling capabilities for complex problems through adaptive problem decomposition into simpler subproblems. Specifically, we develop a modeling tree that organizes a wide range of OR problems based on their hierarchical problem taxonomy and complexity, with each node representing a problem category and containing relevant high-level modeling thoughts. Given a problem to model, we recurrently search the tree to identify a series of simpler subproblems and synthesize the global modeling thoughts by adaptively integrating the hierarchical thoughts. Experiments show that OptiTree significantly improves the modeling accuracy compared to the state-of-theart, achieving over 10% improvements on the challenging benchmarks.

These appendices contain demonstrations of the results in the main text as well as additional technical notes.