Reasoning LLMs Deliver Value Today, So AGI Hype Doesnt Matter Reasoning LLMs are a relatively new and interesting twist on the genre They are demonstrably able to solve a whole bunch of problems that previous LLMs were unable to handle, hence why we've seen a rush of new models from OpenAI and Anthropic and Gemini and DeepSeek and Qwen and Mistral
Apple Exposes the Hype: LLMs Cannot Reason. What You Need to Know About . . . In a paper aptly titled The Illusion of Thinking, Apple researchers aimed to measure the true reasoning capabilities of several leading “reasoning-enhanced” LLMs, models like OpenAI’s GPT-4, Claude 3 7 Sonnet from Anthropic, Google’s Gemini Thinking, and IBM Granite
Apple Challenges AI Reasoning Hype: Exposes Limitations of LLMs Apple challenges the hype around AI reasoning models, exposing their limitations through rigorous testing The research paper reveals reasoning models struggle with complex problems, questioning the path to true artificial general intelligence (AGI)
Apple’s Latest AI Study Strikes at the Heart of “Reasoning” Model Hype Apple’s classification of reasoning performance shows a stark transition across task complexity: Low Complexity Standard LLMs without chain-of-thought outperform LRMs Reasoning models overthink simple tasks: they find the answer, then double-back through wrong paths, losing performance and efficiency Medium Complexity LRMs gain an edge here
New paper pushes back on Apple’s LLM ‘reasoning collapse’ study The Apple paper has been widely cited as proof that today’s LLMs fundamentally lack scalable reasoning ability, which, as I argued here, might not have been the fairest way to frame the study in