arXiv:2605.26647v1 Announce Type: new Abstract: Feedforward network (FFN) layers account for a large fraction of parameters and nonlinear expressivity in Transformer-based large language models (LLMs). Despite the evolution from ReLU and GELU to gated variants such as SwiGLU, most FFN designs still use a single fixed activation function, applying the same nonlinear transformation to all tokens. In this work, we propose Mixture of Activations (MoA), a token-adaptive FFN design that mixes a dictionary of activation functions using lightweight input-dependent gates while sharing the same linear p
The continuous drive for efficiency and performance in large language models necessitates constant innovation in their foundational architectural components, such as feedforward layers, pushing researchers to explore more dynamic and adaptive designs.
Improving the expressivity and efficiency of FFNs directly translates to more capable and resource-optimized LLMs, which are central to AI development, potentially reducing inference costs and enhancing model performance.
FFN designs in Transformers may evolve from fixed activation functions to token-adaptive mixing of activations, offering more nuanced and efficient non-linear transformations for different input tokens.
- · AI model developers
- · Cloud computing providers (through efficiency gains)
- · Hardware manufacturers (optimized for new FFN architectures)
- · Legacy FFN architectures (if MoA becomes dominant)
- · Developers relying solely on older, less efficient LLM designs
More efficient and powerful large language models become feasible due to improved foundational architecture.
Reduced computational costs for AI inference could democratize access to advanced AI capabilities.
The enhanced performance of LLMs contributes to the acceleration of AI agents' capabilities and broader AI applications.
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Read at arXiv cs.LG