This research paper introduces the Laws of Reasoning (LoRe), a theoretical framework to analyze reasoning behaviors in Large Reasoning Models (LRMs). It proposes two key laws: compute law (reasoning compute should scale linearly with question complexity) and accuracy law. Since question complexity is hard to quantify, the researchers developed LoRe-Bench to measure two tractable properties—monotonicity and compositionality. Evaluation reveals most reasoning models show reasonable monotonicity but lack compositionality, highlighting areas for improvement in AI reasoning capabilities.
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