RiddleBench: A New Generative Reasoning Benchmark for LLMs
Authors: name: Deepon Halder, name: Alan Saji, name: Thanmay Jayakumar, name: Ratish Puduppully, name: Anoop Kunchukuttan, name: Raj Dabre
Organization: AI4Bharat

Large Language Models have demonstrated strong performance on many
established reasoning benchmarks. However, these benchmarks primarily evaluate
structured skills like quantitative problem-solving, leaving a gap in assessing
flexible, multifaceted reasoning abilities that are central to human
intelligence. These abilities require integrating logical deduction with
spatial awareness and constraint satisfaction, which current evaluations do not
measure well. To address this, we introduce RiddleBench, a benchmark of 1,737
challenging puzzles in English designed to probe these core reasoning
capabilities. Evaluation of state-of-the-art models on RiddleBench shows
fundamental weaknesses. Even top proprietary models like Gemini 2.5 Pro, o3,
and Claude 4 Sonnet achieve accuracy just above 60% (60.30%, 63.37%, and
63.16%). Analysis further reveals deep failures, including hallucination
cascades (accepting flawed reasoning from other models) and poor
self-correction due to a strong self-confirmation bias. Their reasoning is also
fragile, with performance degrading significantly when constraints are
reordered or irrelevant information is introduced. RiddleBench functions as a
diagnostic tool for these issues and as a resource for guiding the development
of more robust and reliable language models.