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This paper introduces Response Renormalization, a backward-pass framework to improve training stability in Deep Equilibrium Models by addressing near-singular Jacobian issues, demonstrated across various multiphysics applications.
This paper introduces Attractor Models, which use fixed-point solving and implicit differentiation for efficient iterative refinement, achieving superior language modeling and reasoning performance with reduced computational costs compared to traditional transformers.