The WorkBuddy Bench framework validates coding agents across four practical domains with contamination-resistant task construction and full reproducibility through open publication.
A structured evaluation framework helps security leaders assess AI-SOC platforms not only on features but on actual production suitability in their own environment.
A specialized benchmark with 235 tasks reveals that established benchmarks systematically overestimate or ignore significant weaknesses in modern AI models.
Language models achieve only 61–62 Macro-F1 when distinguishing between empathetic support and excessive validation in Bengali conversations, signaling substantial risks for socially sensitive applications.
While video generation models produce visually convincing movements, visual quality does not correlate with practical executability by robots — an evaluation criterion overlooked by standard…
Current frontier models achieve less than 50 percent success rate on the new ITBench-AA benchmark for evaluating agentic IT capabilities, revealing a significant gap between…
