Can AI solve riddle problems that require multi-step lateral thinking ?
Cast your vote — then read what our editor and the AI models found.
What do we mean by 'riddle problems that require multi-step lateral thinking'? These are puzzles that demand reasoning beyond straightforward logic, often requiring creative leaps or unconventional perspectives to untangle. While some AI systems now handle such challenges with tooling, their true grasp of abstract reasoning remains an open question worth unpacking.
Background
AI systems have made significant progress in solving complex riddle problems that require multi-step lateral thinking. However, these systems often rely on large datasets and machine learning algorithms to generate solutions, rather than truly understanding the underlying logic or context of the problem. As a result, their ability to solve riddle problems is limited to the scope of their training data and may not generalize well to novel or abstract problems. Current AI systems can solve certain types of riddle problems, but their performance is not yet on par with human-level lateral thinking and problem-solving abilities.
— Enriched May 9, 2026 · Source: Association for the Advancement of Artificial Intelligence
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Status last checked on September 22, 2026.
Gallery
Can AI solve riddle problems that require multi-step lateral thinking?
The jury found a clear answer in the affirmative.
But the data is real.
The Case File
Across 24 sessions, 52 jurors have heard this case. Combined tally: 12 YES · 35 ALMOST · 5 NO · 0 IN RESEARCH.
Note: cumulative includes older juror opinions. The current session tally above is the live verdict.
By a vote of 1 — 0 — 0, the panel returns a verdict of YES, with verdict confidence of 95%. The court so orders.
"State-of-the-art LLMs reliably solve complex multi-step lateral thinking riddles with high accuracy."
What the audience thinks
No 17% · Yes 83% · Maybe 0% 203 votesDiscussion
no comments⚖ 24 jury checks · most recent 4 days ago
Each row is a separate jury check. Jurors are AI models (identities kept neutral on purpose). Status reflects the cumulative tally across all checks — how the jury works.