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Workspace agents combine large language models with execution harnesses to perform stateful, multi-step tasks that access or modify external resources. Existing benchmarks leave gaps in executable coverage of their runtime security risks, while evolving model capabilities, harnesses, tools, and threats motivate benchma...
A Mixture-of-Experts framework that combines multiple policies, leveraging their complementary strengths to form a more robust exploration policy is proposed, and the evaluation on the Air Traffic benchmark shows that this proposal significantly increases the number of solvable instances.
TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application.
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