According to @godofprompt, the new research paper 'Chain-of-Visual-Thought (COVT)' introduces a breakthrough method for Visual Language Models (VLMs) by enabling them to reason using continuous visual tokens instead of traditional text-based chains of thought. This approach allows models to generate mid-thought visual latents such as segmentation cues, depth maps, edges, and DINO features, effectively giving the model a 'visual scratchpad' for spatial and geometric reasoning. The results are significant: COVT models achieved a 14% improvement in depth reasoning, a 5.5% boost on CV-Bench, and major gains on HRBench and MMVP benchmarks. The technique is compatible with leading VLMs like Qwen2.5-VL and LLaVA, with interpretable visual tokens that can be decoded for transparency. Notably, the research finds that traditional text-only reasoning chains actually degrade visual reasoning performance, whereas COVT’s visual grounding enhances accuracy in counting, spatial understanding, 3D awareness, and reduces hallucinated outputs. These findings point to transformative business opportunities for AI solutions requiring fine-grained visual analysis, accurate object recognition, and reliable spatial intelligence, especially in fields like robotics, autonomous vehicles, and advanced multimodal search. (Source: @godofprompt, Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens, 2025)

According to Fei-Fei Li (@drfeifei) on Twitter, spatial intelligence is emerging as the next major frontier in artificial intelligence, enabling machines to move from simple perception to complex reasoning and actionable understanding. Fei-Fei Li highlights the importance of world models, which allow AI systems to not only interpret visual data but also reason about spatial relationships, predict future scenarios, and create novel solutions. This evolution will drive new business opportunities in robotics, autonomous vehicles, smart manufacturing, and immersive virtual environments, as spatially intelligent AI systems bridge the gap between visual input and practical output. Fei-Fei Li emphasizes that building robust world models is key to unlocking these capabilities and transforming industries (source: Fei-Fei Li Twitter, Nov 10, 2025).

According to Fei-Fei Li (@drfeifei), the recent Stanford HAI alumni event brought together experts and alumni from around the world to discuss practical applications of AI in education reform, workforce transformation, spatial intelligence, and robotics (source: @drfeifei, Oct 19, 2025). The discussions highlighted how AI is driving personalized learning, enabling adaptive educational platforms, and reshaping future job markets through automation and intelligent systems. The event also emphasized emerging business opportunities in robotics and AI-driven spatial analysis, indicating a growing market for startups and enterprises focused on AI applications in education and workforce development (source: Stanford HAI, @StanfordHAI).