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CyberArena is a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat, and introduces a dynamical model that maps match dynamics into unified, multidimensional scores across offensive, defensive, and domain-specific capabilities.
GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal how the gap relates to design discovery and implementation. We introduce D2K-Bench, a diagnostic benchmark of 26 tasks and 85 workloads that measures how effectively agents...
Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes, and scale-matched training retains gains as mechanism size and horizon grow, indicating the potential for an evolving training substrate.
BabelFlow is introduced, a benchmark-general agentic workflow that adapts existing agent benchmarks to new languages by analyzing runtime dependencies, coordinating structure-preserving translation, and combining multi-layer verification with human review to preserve task and evaluation semantics.
E-Commerce Bench is introduced, the first open-source benchmark that integrates multi-round counterpart negotiation and dynamic events into a year-long business operation, and it is found that no single model dominates.
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