Title: Design Principles for Sequence Models via Coefficient Dynamics
Primary area: foundation or frontier models, including LLMs
Keywords: Sequence models, Design principles, Coefficient dynamics

Abstract: Deep sequence models, ranging from Transformers and State Space Models  (SSMs) to more recent approaches such as gated linear RNNs, fundamentally compute outputs as linear combinations of past value vectors. To draw insights and systematically compare such architectures, we develop a unified framework that makes this output operation explicit, by casting the linear combination coefficients as the outputs of autonomous linear dynamical systems driven by impulse inputs. This viewpoint, in spirit substantially different from approaches focusing on connecting linear RNNs with linear attention, reveals a common mathematical theme across diverse architectures and crucially captures softmax attention, on top of RNNs, SSMs, and related models. In contrast to new model proposals that are commonly evaluated on benchmarks, we derive design principles linking architectural choices to model properties. Thereby identifying tradeoffs between expressivity and efficient implementation, geometric constraints on input selectivity, and stability conditions for numerically stable training and information retention. By connecting several insights and observations from recent literature, the framework both explains empirical successes of recent designs and provides guiding principles for systematically designing new sequence model architectures.

TL;DR: Mixer blocks compute linear combinations of value vectors; we model their coefficients as outputs of autonomous linear dynamics with impulse inputs, unifying models from attention to SSMs and yielding design principles for sequence model design.