Logic prompts expose reasoning gaps in LLMs

The failure of "reasoning" models on basic spatial tasks suggests that current architectures prioritize linguistic probability over genuine world-modeling.

• –Slight phrasing variations can cause models to lose track of logical dependencies.
• –Spatial reasoning remains a major hurdle for models that lack physical grounding.
• –Technical benchmarks like the Apple A6 "Swift" test distinguish expert knowledge from generic summaries.
• –Fresh, non-training data prompts are essential to combat benchmark contamination.