Measuring Reasoning in LLMs: a New Dialectical Angle

What does it truly mean for a language model to "reason"? Most current evaluations and benchmarks reward models' correct standalone answers--but correctness alone reveals little about the process that produced them. In this work, we explore a different perspective: reasoning is not a static chain of steps, but a dynamic trajectory where ideas interact, clash, and evolve into deeper insights. To capture this dynamic, we draw on a well-established philosophical tradition: dialectics, where reasoning unfolds through thesis, antithesis, and synthesis. Building on this, we present SIEV, a structured framework that evaluates reasoning of LLMs through dialectics. Unlike conventional evaluations, SIEV assesses not only the conclusion a model reaches, but how it gets there: its ability to resolve tension, integrate distinct ideas, and synthesize higher-order reasoning. This lens uncovers significant reasoning gaps in state-of-the-art models even under saturated benchmarks like GSM and MMLU. For instance, GPT -5-chat, a recent model, loses over 40 points (out of 100) when evaluated with SIEV on GSM. Our findings highlight that adopting a process-oriented, philosophically grounded approach enables a deeper, more rigorous, and more discriminative assessment of LLM reasoning. Reasoning and LLMs: Reasoning is central to how people solve problems and make decisions, and it is increasingly vital for LLMs in real-world use. Traditionally, LLM performance has been assessed using benchmarks that span diverse domains (e.g., GPQA Rein et al. (2023), MMLU-Pro Wang et al. (2024), AIME HuggingFaceH4 (2024), etc.). While these benchmarks offer various metrics to cover comparing models in wide range of topics, the core evaluation paradigm remains largely unchanged: did the model get the right answer? We argue that this narrow focus only on the direct standalone responses is increasingly inadequate--especially when evaluating reasoning. It overlooks the depth, robustness, and coherence of the reasoning process itself. To address this, a shift toward evaluating how models reason--not just what they conclude--is needed.