The article discusses the current state of AI and its comparison to the efficiency of the human brain. It critiques the heavy power and cost demands of existing AI infrastructure while suggesting a future where AI capabilities become more efficient and accessible, potentially diminishing reliance on centralized data centers.

The article examines emerging alternatives to traditional autoregressive transformer-based LLMs, highlighting innovations like linear attention hybrids and text diffusion models. It discusses recent developments in model architecture aimed at improving efficiency and performance.

This article presents Render-of-Thought (RoT), a framework that converts textual reasoning steps into images to clarify the reasoning process of Large Language Models. By using existing Vision Language Models as anchors, RoT achieves significant token compression and faster inference without needing extra pre-training. Experiments show it performs competitively in reasoning tasks.