Maligned - November 28, 2025

AI news without the BS

Here’s what actually matters in AI today. No fluff, no hype - just 5 developments worth your time.

Today’s Top 5 AI Developments

1. Orchestrators Beat Giants: Smaller Models Take the Lead 🏆

Forget scaling up massive LLMs for every complex task. ToolOrchestra shows an 8B model can orchestrate other models and tools, outperforming GPT-5 on tough benchmarks like Humanity’s Last Exam while being 2.5x more efficient. This is a game-changer for practical, cost-effective agentic AI.

Source: arXiv Link: https://arxiv.org/abs/2511.21689v1

2. Google’s Gemini: A New Multimodal AI Powerhouse 🚀

Google has unleashed Gemini, a next-generation multimodal AI model poised to compete directly with the likes of GPT-4. It handles text, code, audio, image, and video, demonstrating advanced reasoning capabilities across modalities and setting a new bar for AI’s general intelligence.

Source: Google DeepMind Link: https://deepmind.google/technologies/gemini/

3. Robots Learn Faster from Diverse Videos 🤖

Teaching robots new tasks is a data nightmare. TraceGen cuts through this by letting robots learn from vast cross-embodiment videos (humans, other robots) using a compact 3D “trace-space.” It adapts to new tasks with just five demonstrations, drastically accelerating robot learning without needing perfect data.

Source: arXiv Link: https://arxiv.org/abs/2511.21690v1

4. Generative AI Gets Precision Control 🎨

Tired of diffusion models guessing your intent? Canvas-to-Image introduces a unified framework for ultra-precise image generation. It takes text, spatial layouts, poses, and subject references all at once, letting you dictate exactly what appears where, finally delivering on truly controlled image synthesis.

Source: arXiv Link: https://arxiv.org/abs/2511.21691v1

5. Agentic AI Remembers & Learns from Mistakes 🧠

Current AI agents often repeat errors. ViLoMem gives multimodal LLMs a true semantic memory, learning from past successes and failures by storing both visual distraction patterns and logical reasoning errors. This dual-stream approach enables agents to build robust knowledge, moving towards more intelligent, lifelong learning.

Source: arXiv Link: https://arxiv.org/abs/2511.21678v1

That’s it for today. Stay aligned. 🎯

Maligned - AI news without the BS