Точка Синхронізації

Researchers specializing in artificial intelligence published a new study on the arXiv preprint server on February 10, 2025, introducing JADE, a novel expert-grounded dynamic evaluation framework designed to address the critical challenges of assessing AI agents on open-ended professional tasks. The development of JADE stems from a persistent dilemma in the field of machine learning, where existing evaluation methods either rely on rigid, static rubrics that fail to account for creative problem-solving or depend on unstable 'LLM-as-a-judge' systems that are prone to internal biases. This new methodology aims to bridge that gap by mimicking the nuanced judgment process of human subject matter experts. The core functionality of JADE rests on its ability to move beyond binary or pre-defined scoring systems. Traditional static rubrics, while offering reproducibility, are often too narrow to capture the breadth of valid strategies an AI agent might employ to solve complex, professional-grade problems. Conversely, when large language models are used as the sole evaluators, they frequently lack the consistency required for scientific rigor and can exhibit systemic biases that skew performance metrics. JADE seeks to resolve these issues by implementing a dynamic, claim-level assessment strategy that evaluates the specific assertions made by an AI agent against core domain-grounded principles. By drawing inspiration from how human experts grade complex work, JADE integrates domain-specific grounding with a flexibility that allows for diverse response types. This approach ensures that AI agents are not penalized for unconventional but correct reasoning, while still maintaining a high standard of professional accuracy. This framework represents a significant step forward in the reliable deployment of agentic AI systems in professional sectors—such as law, medicine, or engineering—where open-ended tasks are common and the cost of error or poor evaluation is exceptionally high.