Ben Santora concludes his AI reasoning series with four diagnostic puzzles designed to expose how different large language models handle contradictions. Tested across ChatGPT, Gemini, Deepseek, KIMI, Qwen, and Cerebras, the puzzles target specific weaknesses in spatial, causal, mathematical, and topological reasoning, revealing surprising variations in model responses.

The puzzles include an impossible triangle violating geometry, a time-travel paradox with circular references, an infinite hotel problem challenging cardinality, and a spatial ordering puzzle with cyclic dependencies. Each is crafted to probe where models might favor creative narrative over rigorous logic or misapply finite intuition to infinite sets, acting as practical tools for developers.

These stress tests highlight that LLMs don't reason uniformly. By feeding these challenges to any system, developers can diagnose specific failure modes—from triangle inequality violations to transitive reasoning breakdowns. The exercise underscores the importance of targeted evaluation beyond standard benchmarks, offering a hands-on method to understand a model's reasoning architecture and its practical limits.