Recently, there has been a lot of hullabaloo about the idea that large reasoning models (LRM) are unable to think. This is mostly due to a research article published by Apple, "The Illusion of Thinking" Apple argues that LRMs must not be able to think; instead, they just perform pattern-matching. The evidence they provided is that LRMs with chain-of-thought (CoT) reasoning are unable to carry on the calculation using a predefined algorithm as the problem grows.This is a fundamentally flawed argument. If you ask a human who already knows the algorithm for solving the Tower-of-Hanoi problem to solve a Tower-of-Hanoi problem with twenty discs, for instance, he or she would almost certainly fail to do so. By that logic, we must conclude that humans cannot think either. However, this [...]
Microsoft on Tuesday released Phi-4-reasoning-vision-15B, a compact open-weight multimodal AI model that the company says matches or exceeds the performance of systems many times its size — while co [...]
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]
Researchers at MiroMind AI and several Chinese universities have released OpenMMReasoner, a new training framework that improves the capabilities of language models in multimodal reasoning.The framewo [...]
Deploying AI agents for repository-scale tasks like bug detection, patch verification, and code review requires overcoming significant technical hurdles. One major bottleneck: the need to set up dynam [...]
The trend of AI researchers developing new, small open source generative models that outperform far larger, proprietary peers continued this week with yet another staggering advancement.Alexia Jolicoe [...]
Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning task [...]
Training a foundation LLM from scratch costs millions and requires internet-scale data — which is why most enterprises don't bother. Sapient thinks it has a cheaper path.To overcome this brute- [...]