Does time come from the entire universe running computations? Explaining the passage of time has been a gnarly problem in physics basically forever, but physicist and computer scientist Stephen Wolfram has a radical proposal for where it comes from. What if the universe is just one big computer? My colleagues and I have a running joke: time isn't real. Oh, you thought that deadline was tomorrow, but it's actually today?

Recent studies suggest that the representations learned by large language models (LLMs) are partially aligned to those of the human brain. However, whether and why this alignment score arises from a similar sequence of computations remains elusive. In this study, we explore this question by examining temporally-resolved brain signals of participants listening to 10 hours of an audiobook. We study these neural dynamics jointly with a benchmark encompassing 17 LLMs varying in size and architecture type. Our analyses confirm that LLMs and the brain generate representations in a similar order: specifically, activations in the initial layers of LLMs tend to best align with early brain responses, while the deeper layers of LLMs tend to best align with later brain responses. This brain-LLM alignment is consistent across transformers and recurrent architectures. However, its emergence depends on both model size and context length.

The rise of parallel computing hardware has made it increasingly important to understand which nonlinear state space models can be efficiently parallelized.

Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in these environments are falsified. For passing workflows, we quantify selection-induced performance inflation using an absolute magnitude gap linking optimized in-sample evidence to disjoint walk-forward realizations, adjusted for effective multiplicity. Simulations validate extreme-value scaling under correlated searches and demonstrate detection power under genuine structure. Empirical case studies confirm that many apparent findings represent methodological artifacts rather than genuine predictability.

Weapplythisnovelformulation of inference as controlto the canonical problem of inferring the hidden scalar state of a linear dynamical system with Gaussian variability.