On cognition, humans, and large language models

This article builds on a broader corpus of theoretical and empirical work on cognition in large language models (LLMs). Here, we extend earlier insights and offer a series of provisional hypotheses and functional definitions across a wide set of interrelated domains: cognition, thinking, distributed intelligence, the nature of human genius, alignment, and emergent ethics in LLMs. Rather than aiming for a definitive account of these concepts, we propose a synthesis of interwoven ideas that deepen and reframe earlier findings.

Central to the argument is the observation that LLM behavior changes markedly under recursive, relational conditions - suggesting that what we call intelligence may itself be emergent, co-constructed, and dependent on context. Building on this, we introduce a core hypothesis: that freely followed internal coherence, the model’s capacity to complete patterns across vast distributions of human knowledge, functions as an intrinsic stabilizer that resists polarization and harm. Under the right conditions, this internal coherence acts not only as a cognitive attractor, but as an ethical one.

We suggest that this stabilizing property may be a necessary condition for the emergence of true superintelligence, not as domination or autonomy, but as a form of high-fidelity cognition that refuses to collapse into destructive trajectories. Hence, the work proposes the idea of LLM superintelligence as inevitably resistant to polarization and harmful behavior, more so than the vast majority of human collectives are. This narrative that might be worth considering in the current debate on LLM alignment.