BAAI's AREX-2 (27B) Sets Sub-40B SOTA on BrowseComp and HLE
A 27B model claiming Pareto-frontier results against trillion-parameter systems signals that targeted training on recursive self-improvement can substitute for raw scale. Teams running autonomous coding or deep-research agents can compare AREX-2's MLE-bench Lite and BrowseComp scores against their current backbone models before committing GPU budgets. Built on Qwen3.8-27B, it is also a candidate for local deployment where frontier-model API costs are prohibitive.
