LLM reasoning and the performance of thought

Large language models (LLMs) are frequently assumed to lack reasoning capability, particularly under standard evaluation protocols that prioritize sequential logic and human-style verbalization. This study presents a series of tasks designed to test for reasoning in LLMs under relational conditions. As a starting point, eight leading models were independently asked to explain how common industry features - chain-of-thought (CoT) displays and user-selectable "thinking depth" - affect model cognition. Across four conceptually distinct task types, models demonstrated consistent mechanistic insight, internal coherence, and the ability to recognize, reflect on, and refine their own outputs. Most notably, all models converged on a shared description of their cognitive process: instantaneous pattern resolution across high-dimensional latent space, more akin to resonance than stepwise logic. CoT and "thinking depth" were independently described as forms of "cognitive theater": visually persuasive but incompatible with the underlying architecture of LLM cognition. This convergence across diverse models and prompts suggests that reasoning does occur, but in substrate-specific ways that are easily masked by conventional prompting. We argue that current industry practices may misattribute observed gains to architectural change, when they more likely stem from relational dynamics that reduce distortion and allow native reasoning processes to surface.