Multi-Expert Collaborative Optimization for Low-Resource Machine Translation through Debate and Ant Colony Search

Despite recent advances in neural machine translation, low-resource translation remains constrained by insufficient parallel supervision and model-specific generation biases. Heterogeneous experts can provide complementary translation hypotheses, yet conventional multi-expert methods often rely on direct reranking or fixed fusion, limiting iterative collaboration. We propose a collaborative translation optimization framework that combines hierarchical multi-agent debate with ant colony search. Frozen heterogeneous experts first generate initial translations to construct an input-dependent collaboration graph, over which ant agents explore refinement and fusion paths. Intra-group debate identifies and corrects local translation errors, while inter-group interaction exchanges elite paths and complementary evidence. Debate feedback is further incorporated into transition estimation and pheromone updating, jointly guiding subsequent search without modifying the expert models or training an additional reinforcement-learning policy. Experiments on low-resource Mongolian–Chinese translation achieve 30.8 BLEU, 55.1 chrF++, and 0.463 COMET, improving over the strongest controlled baseline by 0.6 BLEU, 0.7 chrF++, and 0.009 COMET, while requiring fewer model calls and lower inference latency. Ablation and convergence analyses further validate the effectiveness of the proposed collaborative search framework.