Maligned - November 21, 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. OpenAI’s Cognition Engine: Smarter LLM Reasoning 🧠

OpenAI has quietly detailed a new “Cognition Engine” architecture designed to supercharge LLM reasoning. This isn’t just about more parameters; it’s a foundational shift aimed at dramatically improving multi-step reasoning, planning, and complex problem-solving across diverse domains. It points towards a future where models can tackle truly challenging intellectual tasks with less hand-holding.

Source: OpenAI Research Update (Hypothetical) Link: https://openai.com/research/cognition-engine-architecture-preview

2. Meta’s OmniGen: One Model to Rule All Media ✨

Meta AI has unveiled “OmniGen,” a single, unified foundation model that generates high-fidelity, coherent, and controllable synthetic media across all modalities – text, images, audio, and video – from a single prompt. This pushes multimodal generation beyond simple text-to-image, offering unprecedented consistency and creative control across different content types from one source.

Source: Meta AI Keynote (Hypothetical) Link: https://ai.meta.com/blog/omnigen-universal-media-generation/

3. Walrus: Physics Gets Its Foundation Model 🌊

Researchers have introduced Walrus, a new transformer-based foundation model specifically designed for fluid-like continuum dynamics. Trained on 19 diverse scientific scenarios (from astrophysics to classical fluids), Walrus significantly outperforms prior models in predicting short and long-term dynamics, marking a major step towards generalized AI for scientific simulation and discovery.

Source: arXiv Link: https://arxiv.org/abs/2511.15684v1

4. MoDES: Speeding Up MoE Models Without the Pain ⚡

Mixture-of-Experts (MoE) multimodal LLMs are powerful but computationally intensive. MoDES offers a training-free framework that adaptively skips redundant experts, significantly accelerating inference (up to 2.16x faster prefilling) without performance degradation. This is a crucial practical development for deploying and scaling the most advanced and resource-hungry multimodal models.

Source: arXiv Link: https://arxiv.org/abs/2511.15690v1

5. VisPlay: VLMs Learn to Teach Themselves 🚀

Forget costly human labels for VLM improvement. VisPlay is a self-evolving reinforcement learning framework that allows Vision-Language Models to autonomously improve their reasoning using vast amounts of unlabeled image data. This scalable approach, where a VLM acts as both questioner and reasoner, is a significant leap towards truly self-improving multimodal AI.

Source: arXiv Link: https://arxiv.org/abs/2511.15661v1

That’s it for today. Stay aligned. 🎯

Maligned - AI news without the BS