Chang Xia | Search arXiv

arXiv2026

A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support

Large language models (LLMs) show potential for medical tasks, but their single-turn question-answer format does not reflect how clinical diagnosis is performed in practice. As a result, they remain limited in complex diagnostic settings. We developed Debate-Mixture-of-Agents (DMoA), a novel multi-agent framework that structures role-based interaction to support iterative diagnostic reasoning. Base models and DMoA were evaluated on 297 rare disease cases and 1,719 challenging cases. Across both datasets, DMoA improved most likely diagnosis accuracy by 10.21 percentage points and safety rate by 11.36 percentage points over GPT-4o baseline. Ablation experiments showed that the gains were not simply due to the use of more models or longer outputs, but also reflected the contribution of the structured workflow. Further analyses examined how framework design, base model choice, and token budget affected performance. DMoA performed better with a 4*2 structure, stronger base models, and a larger token budget. These findings demonstrate the potential of DMoA for clinical tasks and suggest further investigation of multi-agent frameworks.

cs.CL↗

arXiv2026

CAT-LDP: Cloud-edge Adaptive Taxonomy under Local Differential Privacy

Recommender systems are widely used in daily life, but their direct collection and use of user preference data can also lead to privacy leakage. Existing privacy-preserving recommendation methods often find it hard to balance user privacy and recommendation performance. This problem is more serious in implicit-feedback settings, where data sparsity further increases the loss of useful signals caused by privacy perturbation. To solve this problem, we propose CAT-LDP, a cloud-local collaborative recommendation framework under local differential privacy constraints. CAT-LDP combines a hierarchical taxonomy tree with an adaptive privacy budget allocation strategy to keep more useful signals in users' active categories while protecting user privacy. Specifically, users upload perturbed category profiles that satisfy LDP. Based on these profiles, the cloud performs coarse-grained candidate generation, and the local device then carries out fine-grained reranking by using unperturbed local history. Experiments on the Amazon Video Games dataset show that CAT-LDP consistently outperforms its fixed-budget ablation variant and representative baselines on HR@K and NDCG@K under different privacy budgets. The results show that combining category-space modeling with cloud-local task decoupling can effectively reduce noise amplification in long-tail sparse settings and provide a better balance between privacy and utility for implicit-feedback recommendation.

cs.DB↗