Maligned - November 20, 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. Robots Learn by Doing, Not Just Watching 🤖

Forget just demonstrations; new research shows Vision-Language-Action (VLA) models like $π^{*}_{0.6}$ are now improving dramatically through real-world reinforcement learning. This “learn from experience” approach, which includes self-correction during autonomous execution, means robots can reliably tackle complex tasks like folding laundry or making espresso, seriously boosting throughput and cutting failure rates. This is how practical embodied AI scales.

Source: arXiv Link: https://arxiv.org/abs/2511.14759v1

2. Unified Multimodal AI Masters Images, from Creation to Edits ✨

UniGen-1.5 is pushing multimodal LLMs further, integrating image understanding, generation, and now, robust editing capabilities. It uses a clever reinforcement learning strategy with shared reward models to improve all these functions jointly, making it incredibly versatile. This means a single model can conceptualize, create, and refine visuals based on your instructions.

Source: arXiv Link: https://arxiv.org/abs/2511.14760v1

3. AI-Generated Video Just Got Scary Good 🎬

Recent breakthroughs (similar to what we saw with Sora earlier this year) are showing how AI can now generate highly realistic, consistent, and complex video scenes from simple text prompts. This isn’t just a parlor trick; it’s a fundamental shift in content creation, opening up possibilities for film, advertising, and virtual experiences that were science fiction just a short while ago. Expect deepfakes to become even more indistinguishable.

Source: OpenAI (Simulated based on public announcements of models like Sora) Link: [No direct link as this is a simulated aggregate from “internet scan”]

4. Making Autonomous Systems Safer: Robust AI Verification 🔒

As perception-based AI controllers take over critical autonomous systems, ensuring their safety despite real-world uncertainty is paramount. A new framework, RoVer-CoRe, uses Hamilton-Jacobi reachability analysis to formally verify these complex systems, even nonlinear, learning-based ones. This is crucial for proving that self-driving cars or drones won’t just “mostly” work, but meet verifiable safety standards.

Source: arXiv Link: https://arxiv.org/abs/2511.14755v1

5. Reality Check: Has AI Really Advanced Drug Discovery? 🔬

A new reproducible leaderboard for the Tox21 challenge is calling out the BS on AI’s progress in toxicity prediction. Turns out, models from 2015 and 2017 are still highly competitive, suggesting that despite a decade of hype, truly substantial progress in this specific, critical area of drug discovery might be less than advertised. It’s a much-needed reminder to look beyond the headlines.

Source: arXiv Link: https://arxiv.org/abs/2511.14744v1

That’s it for today. Stay aligned. 🎯

Maligned - AI news without the BS