AI evaluation · Research analysis AI agents increasingly solve coding exercises, browse websites, manipulate files, and operate desktop interfaces. Yet strong benchmark results still rarely translate into reliable execution of economically meaningful professional work. The gap is not only a model problem. It is also an evaluation problem: many benchmarks measure
Modern multimodal models can describe videos, answer questions about them, and reason over visual and audio streams. But do they really listen? Or do they merely infer what the sound should be from what they see? This article discusses the paper When Vision Speaks for Sound , which investigates a hidden
Deep learning has become one of the most successful engineering paradigms in modern artificial intelligence. Yet its theoretical foundations remain incomplete. We know how to train large models, but we still lack a unified scientific account of why they learn, how their internal representations form, and why their performance follows
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The rise of AI agents is gradually transforming how language models are used in production. More recently, personal agents such as OpenClaw have redefined the personal use cases of agentic AI. Hermes Agent positions itself as an ambitious evolution of this kind of agent. Developed by Nous Research, it promises