Utilize AI to analyze application runtime data (e.g., rendering time, communication latency), obtain optimization suggestions (such as reducing component re-rendering, reusing hardware connections), ...
Abstract: This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework’s performance was assessed on three key metrics: effectiveness and ...
We evaluate DeepCode on the PaperBench benchmark (released by OpenAI), a rigorous testbed requiring AI agents to independently reproduce 20 ICML 2024 papers from scratch. The benchmark comprises 8,316 ...
This brief showcases Bloomberg Terminal’s broader regulatory and policy coverage related to risk, capital and financial stability across markets markets such as Australia, UAE, the UK and New Zealand.
Manchester researchers have developed a systematic methodology to test whether AI can think logically in biomedical research, ...
What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better ...
ASTM published Standard E3499-25 for PIP testing, enabling faster mechanical property analysis in regulated manufacturing ...
Testing AI systems is hard. Responses are non-deterministic, you need to validate tool usage, and semantic meaning matters more than exact text matching.
Abstract: Deep learning models are rapidly advancing and finding widespread applications in various critical domains. Ensuring the security of these models has garnered significant attention from both ...
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