Beyond Behavior: Why AI Evaluation Needs a Cognitive Revolution

arXiv:2608.18081v1 Announce Type: new Abstract: Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and

arXiv:2608.00794v1 Announce Type: new Abstract: Agentic AI systems are evaluated using automated benchmarks whose scores justify deployment decisions, safety certifications, and

arXiv:2605.11496v1 Announce Type: new Abstract: Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently

arXiv:2608.00680v1 Announce Type: new Abstract: Evaluating artificial intelligence systems has historically relied on outcome-based benchmarks that measure task accuracy,

arXiv:2605.04454v1 Announce Type: new Abstract: Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under

arXiv:2608.13577v1 Announce Type: new Abstract: This position paper argues that the dominant paradigm of AI evaluation (which focuses on superhuman autonomous performance and so

arXiv:2605.01604v1 Announce Type: new Abstract: Existing evaluation frameworks for large language models -- including HELM, MT-Bench, AgentBench, and BIG-bench -- are designed for

arXiv:2603.20639v1 Announce Type: new Abstract: The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is