Reasoning's Razor: When Thinking More Makes Safety Worse

Large Reasoning Models (LRMs) like DeepSeek-R1 and QwQ-32B have become remarkably capable at solving complex problems through extended chain-of-thought. The natural instinct is to apply this power to safety-critical tasks: detecting harmful content, catching hallucinations, flagging policy violations. More reasoning = more accuracy = safer AI, right?

A new paper challenges that intuition head-on. “Reasoning’s Razor”

How Can LoRA parameters improve the detection of Near-OOD data?

We’ve all come to love Low-Rank Adaptation (LoRA) for making it practical to fine-tune massive Large Language Models (LLMs) on our own data. The standard practice is simple: you inject small, trainable matrices into the model, fine-tune only them, and then, for deployment, you merge these new weights back into the original model to avoid any inference...

Weight Space Learning Treating Neural Network Weights as Data

In the world of machine learning, we often think of data as the primary source of information. But what if we started looking at the models themselves as a rich source of data? This is the core idea behind weight space learning, a fascinating and rapidly developing field of AI research. The real question in this post...