Seven replies to the viral Apple reasoning paper – and why they fall short
15 hours ago
- #AI
- #Machine Learning
- #Reasoning Models
- The Apple paper on limitations in Large Reasoning Models (LRMs) has sparked widespread discussion and media coverage.
- Seven main rebuttals to the Apple paper were analyzed, ranging from nitpicking to clever arguments, but none were found compelling.
- Key points from the rebuttals include claims about human-like limitations in machines, output token limits in LRMs, and the paper's authorship by an intern.
- The paper's findings suggest that scaling up models may not solve fundamental reasoning issues, and integrating symbolic AI with neural networks is necessary.
- A Salesforce paper corroborates the Apple findings, showing poor performance in multi-turn reasoning tasks.
- Critics argue that the paper's examples are limited, but the author believes more evidence will emerge to support the findings.
- The author highlights the need for better AI systems that combine neural and symbolic approaches for reliable reasoning.