BrainBench: Exposing the Commonsense Reasoning Gap in Large Language Models
Large language models (LLMs) achieve impressive scores on standard benchmarks yet routinely fail questions that any human would answer correctly in seconds. We introduce BrainBench, a benchmark of 100 brainteaser questions spanning 20 carefully designed categories, each targeting a specific commonsense reasoning failure mode in LLMs. Categories range from implicit physical constraints ("Should I walk or drive my rental car to the return lot?") to semantic scope tricks and default assumption hijacks. We evaluate eight frontier models -- four from the Claude family and four from the GPT family -- using a zero-shot protocol with 10 independent runs per question. The best model, Claude Opus 4...
| Authors | Yuzhe Tang |
|---|---|
| Primary category | cs.AI |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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