dynamic-gating

DLLG (Dynamic Logit-Level Gating) is a novel framework that dynamically fuses multiple specialized LLMs at the token-level logit space using a lightweight learned gating module, outperforming routing, heuristic ensembling, and parameter-merging baselines across reasoning and code benchmarks. The approach requires only sparse response-level supervision and preserves expert modularity without retraining.