Neuromodulated Continuous-Coupled Dynamics for Language Understanding

Continuous-coupled neural networks (CCNNs) compute by repeated coupling among neighboring units rather than by stacking layers, making their computation input-conditioned and their intermediate quantities directly inspectable, two properties that language understanding also calls for. However, CCNNs have been applied only to perceptual data, and language does not admit a direct transfer, with token-level units, content-dependent neighborhoods, and input-dependent integration requirements. To address these challenges, we propose the neuromodulated continuous-coupled neural network ($\text{Ne-CCNN}$), a hybrid dynamic architecture in which task-corpus co-occurrence embeddings define the units and a polarity-gated semantic graph infers the neighborhood from content. A neuromodulatory system of about 1,000 parameters supplies the per-input integration through firing thresholds and coupling gain at inference time. A polarity-routed readout attributes the evidence behind each prediction to named channels, and the recurrent update admits a checkable sufficient condition for contraction. Across English and Chinese benchmarks, Ne-CCNN is competitive with matched non-pretrained baselines, with gains of up to 6.8 points on six of the seven sentence-pair tasks, using 1.5–3.0M non-embedding parameters (under 5% of the pre-trained references). Ablations show that its neuromodulatory pathway contributes to accuracy on six of seven benchmarks (neutral on the seventh). Code is available at https://anonymous.4open.science/r/Ne-CCNN/