Standard models today are "one-and-done"—they pass data through a static layer stack and hope for the best. SERN (Self-Equilibrating Recurrent Network) is different. It’s built to "think" internally, using a critic system to refine its own state until it reaches equilibrium before outputting a result. We’re moving away from brute-force scaling. In early testing with algebra solving, a SERN-G prototype reached a solution error margin of just 0.000179. This paper explores how internal state dynamics can make networks faster, more efficient, and better at reasoning.
Pierce Dolan (Tue,) studied this question.