Gemini 2.5 • Ghibli• Reasoning Models

Google’s new Gemini 2.5 AI models enhance reasoning, OpenAI sparks debate with Ghibli-style image generation, and researchers explore better decision-making for AI.

Author

Brightcone AI Team
March 29, 2025

 ✨ Highlights This Week

➡️ Google Unveils Gemini 2.5 – A new AI reasoning model with a 1M-token context window, excelling in coding and web app creation.


➡️ Ghibli-Style AI Images – OpenAI’s new feature lets users generate Studio Ghibli-inspired artwork, sparking both excitement and ethical concerns.


➡️ AI Reasoning Breakthroughs – Research shows in-context learning improves AI decision-making while reducing overthinking.

1.HOT THIS WEEK

Google has launched Gemini 2.5, a new family of AI reasoning models designed to pause and “think” before answering questions. The flagship Gemini 2.5 Pro Experimental model is available in Google AI Studio and for Gemini Advanced subscribers. Google claims the model outperforms competing AI systems on several benchmarks, particularly excelling at creating web apps and agentic coding applications. The model ships with a 1 million token context window (approximately 750,000 words) with a 2 million token version coming soon. Google states that moving forward, all its new AI models will have reasoning capabilities integrated.

OpenAI’s latest feature in ChatGPT enables users to generate images in the distinctive style of Studio Ghibli. This has led to a surge of “Ghibli-fied” images across the internet, as users transform personal photos and popular memes into whimsical, anime-inspired artworks. While this has sparked excitement among fans, it has also raised ethical and copyright concerns, particularly given Studio Ghibli founder Hayao Miyazaki’s known skepticism towards AI-generated art.  

Research Breakthrough: Enhancing Reasoning in Large Language Models

A significant development emerged from arXiv, with recent papers published between March 20 and March 26, 2025, showcasing advancements in AI reasoning. One notable paper, "Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking" by Yuyao Ge et al., highlights how in-context learning can improve LLMs' reasoning capabilities. This method reduces overthinking, enhancing accuracy in complex tasks, and makes decision-making more transparent.

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5.😂 THIS WEEK IN MEMES

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