This is interesting as a first large diffusion-based LLM. Most of the LLMs you've been seeing are ~clones as far as the core modeling approach goes.

They're all trained "autoregressively", i.e. predicting tokens from left to right. Diffusion is different - it doesn't go left to right, but all at once. You start with noise and gradually denoise into a token stream.

Most of the image / video generation AI tools actually work this way and use Diffusion, not Autoregression. It's only text (and sometimes audio!) that have resisted.

So it's been a bit of a mystery to me and many others why, for some reason, text prefers Autoregression, but images/videos prefer Diffusion. This turns out to be a fairly deep rabbit hole that has to do with the distribution of information and noise and our own perception of them, in these domains. If you look close enough, a lot of interesting connections emerge between the two as well.

All that to say that this model has the potential to be different, and possibly showcase new, unique psychology, or new strengths and weaknesses. I encourage people to try it out! Read the entire post

NEW AI FOR HUMANS! -

@amazon

's new Alexa+ is better than ol' Alexa Minus -

@AnthropicAI

's Claude Sonnet 3.7 is VERY fun -

@levelsio

's Flight Sim shows how AI is changing games -

@OpenAI

's Incoming GPT 4.5 AND...

@grok

's Uncensored Mode dates our AI Friend Gash. Links below!Description text goes here. Link to post here.

Welcome to today's Robotics and Autonomous Systems research roundup. Here, we spotlight the most groundbreaking papers shaping the future of intelligent machines and autonomous systems.

Contents

1. Let's Get to the Point: LLM-Supported Planning, Drafting, and Revising of Research-Paper Blog Posts Read the Entire Article

2. Generative AI in Medicine

3. Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners

4. Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning

5. ProxyLLM : LLM-Driven Framework for Customer Support Through Text-Style Transfer

6. Everyday AR through AI-in-the-Loop

7. Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems

8. Large Language Model-Brained GUI Agents: A Survey

9. As Generative Models Improve, We Must Adapt Our Prompts

10. Understanding Emotional Body Expressions via Large Language Models

11. DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving

12. Bots against Bias: Critical Next Steps for Human-Robot Interaction

13. A Scalable Approach to Benchmarking the In-Conversation Differential Diagnostic Accuracy of a Health AI

14. FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal Footstep Planning and Forecasting

Let's Get to the Point: READ the entire article here

Welcome to today's Robotics and Autonomous Systems research roundup. Here, we spotlight the most groundbreaking papers shaping the future of intelligent machines and autonomous systems.

Contents

1. Let's Get to the Point: LLM-Supported Planning, Drafting, and Revising of Research-Paper Blog Posts Read the Entire Article

2. Generative AI in Medicine

3. Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners

4. Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning

5. ProxyLLM : LLM-Driven Framework for Customer Support Through Text-Style Transfer

6. Everyday AR through AI-in-the-Loop

7. Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems

8. Large Language Model-Brained GUI Agents: A Survey

9. As Generative Models Improve, We Must Adapt Our Prompts

10. Understanding Emotional Body Expressions via Large Language Models

11. DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving

12. Bots against Bias: Critical Next Steps for Human-Robot Interaction

13. A Scalable Approach to Benchmarking the In-Conversation Differential Diagnostic Accuracy of a Health AI

14. FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal Footstep Planning and Forecasting

Let's Get to the Point: READ the entire article here

MY MIND HAS JUST BEEN BOGGLED. I just tried the early release of OpenAI's new DeepResearch feature (rolling out later today to the $200/month Pro users). I've been working in the CRM software industry for 30+ years. It's not just that I've had court-side seats to the game, I've been on the court, doing my best to play the game. First in vertical CRM (my first startup) and now as co-founder of HubSpot. I've had some modest success and I feel like I have a pretty good handle on things in the industry. That's why OpenAI's new DeepResearch feature boggled my mind. I asked it create a detailed research report including competitive analysis, positioning, growth, product strategy and AI vision for the industry. What it produced was an 11,000 word report. With data. And citations. And tables. And genuinely great insights -- including some I hadn't really thought of before. Read the entire post here.