BiRG-LoRA achieves 69.31% macro-average accuracy across CMB, CMExam, MedQA, and MedMCQA, outperforming MoELoRA by 0.89 points with 28.1% fewer trainable parameters under a matched Qwen3-8B protocol.
Each rank could be an expert: Single-ranked mixture of experts LoRA for multi-task learning,
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DAG-MoE uses a lightweight module to learn DAG-based structural aggregation of selected experts, expanding combination space and enabling intra-layer multi-step reasoning compared to standard weighted-sum MoE.
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Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering
BiRG-LoRA achieves 69.31% macro-average accuracy across CMB, CMExam, MedQA, and MedMCQA, outperforming MoELoRA by 0.89 points with 28.1% fewer trainable parameters under a matched Qwen3-8B protocol.