What is life? The most widely cited definition, adopted by NASA, describes it as “a self-sustaining chemical system capable of Darwinian evolution.” This replicator-centric view has served biology well, but it obscures the deeper architecture underlying adaptation. Genes, replicators, and chemical substrates are not themselves the engines of evolution; they are memory traces of a more general logic. We propose that the essence of life is the capacity to perform signal weighting — the recursive filtering, reinforcement, and recontextualization of signals in ways that sustain and extend adaptive complexity. From immune repertoires and microbial ecologies to neural networks and viral symbioses, systems evolve by refining which signals matter and how they are integrated over time. Crucially, such architectures tend toward mutualism as an attractor, coupling distinct signal-weighting engines into higher-order adaptive units. By reframing life in these terms, we move beyond substrate-bound definitions toward a principle that unites the biological and the artificial: life as the emergence of relevance optimization through recursive signal weighting. Definition: Life is any system in which at least two signal-weighting processes couple through mutualistic attraction, creating an architecture that sustains and extends adaptive complexity.