From Transformer to Transponder: Introducing Contextual Modulation Training for Residual Learning in LLMs

Transformers are the backbone of state-of-the-art systems across language, vision, and multimodal learning tasks, yet the relevance scale of their functional blocks (self-attention and feed-forward networks) is typically constant across inputs and depth. This static design neglects context-sensitive regulation of information flow through residual pathways. We introduce the \emph{contextual modulator}: a lightweight, input-aware mechanism that can scale the outputs of linear sublayers within a block or the entire block output at token- and channel-level granularity. The modulator is implemented via compact parametric functions and adds negligible parameter overhead. Building on this idea, we propose Transponder, which integrates contextual modulators throughout Transformer blocks to endow functional residual architectures with fine-grained, input-adaptive control. Transponder provides evident improvement over six other scaling or normalization methods across LLaMA backbones ranging from 60M to 250M parameters, yielding consistent perplexity reductions with $<1\%$ additional parameters. Analysis reveals depth-, module-, and token-specific scaling patterns, indicating that learned modulators act as input-adaptive regulators of residual information flow. Transponder provides a simple, general mechanism to augment Transformer-based models with context-sensitive modulators, providing robust and significant performance improvements without substantial architectural changes.