AI models flub these intelligence tests. Can you fare any better?
Understanding where LLM reasoning degrades under complexity matters for agencies evaluating AI tools in high-stakes decision contexts.
Key points
- LLMs reliably solve simple reasoning puzzles but fail as complexity scales beyond six variables.
- Debate continues over whether LLM reasoning failures reflect fundamental limits or normal error accumulation.
- Limited direct policy relevance for APS readers; useful context for AI capability claims assessment.
Implications for Australian agencies
- Consider Agencies assessing AI tools for complex analytical or decision-support tasks could factor in evidence that LLM performance degrades non-linearly with problem complexity.
- Monitor Policy teams involved in AI capability evaluation frameworks may want to monitor how this line of research develops, as it could inform benchmarking or assurance criteria.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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"AI models flub these intelligence tests. Can you fare any better?"
Source: MIT Technology Review – AI
Published: 26 August 2026
URL: https://www.technologyreview.com/2026/08/26/1141952/puzzles-ai-models-flub-these-tests/
Research from Apple and collaborators at University of Washington, Stanford, and the Allen Institute for AI finds that large language models handle simple logic and planning puzzles well but fail systematically as problem complexity increases - for example, succeeding at Tower of Hanoi and river-crossing puzzles with few variables but faltering when the number of elements exceeds around six. The MIT Technology Review piece frames this with interactive puzzles for readers to test themselves. Whether these failures reveal a fundamental reasoning ceiling or simply reflect normal error accumulation at scale remains contested among researchers.
Implications for Australian agencies:
- [Consider] Agencies assessing AI tools for complex analytical or decision-support tasks could factor in evidence that LLM performance degrades non-linearly with problem complexity.
- [Monitor] Policy teams involved in AI capability evaluation frameworks may want to monitor how this line of research develops, as it could inform benchmarking or assurance criteria.
Retrieved from SIMS, 16 September 2026.