Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities. Current approaches struggle with cross-modal alignment, temporal consistency, and robust handling of noisy or incomplete inputs across multiple modalities. We propose MultiAgent-Chain of Thought (CoT), a novel multi-agent chain-of-thought reasoning framework where specialized agents for text, vision, and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms. Our architecture incorporates self-reflection modules, conflict resolution protocols, and dynamic rationale alignment to enhance consistency, factual accuracy, and user engagement. The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity. Comprehensive evaluations on Situated Interactive MultiModal Conversations (SIMMC) 2.0, VisDial v1.0, and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy (p < 0.01), chain-of-thought interpretability, and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.

How can AI assist doctors in generating clinical reports without compromising patient privacy? This question motivates our development of PrivLLM-Guard, a novel framework for differentially private large language models (LLMs) tailored to real-time confidential medical text generation and summarization. While LLMs have shown promise in automating clinical documentation, the sensitivity of healthcare data demands rigorous privacy protections. PrivLLM-Guard addresses this need by combining advanced—differential privacy techniques with adaptive noise calibration, ensuring robust privacy guarantees without sacrificing utility. The framework integrates bidirectional transformer encoders with autoregressive decoders, further enhanced by privacy-aware attention and gradient perturbation mechanisms. Extensive experiments on three large-scale medical datasets demonstrate BLEU-4 scores of 89.7% for generation and ROUGE-L scores of 92.3% for summarization, while maintaining strict privacy budgets. The model processes 512-token sequences in real time with an average latency of 245 ms and memory usage of just 4.2 GB. Compared to state-of-the-art privacy-preserving LLMs, PrivLLM-Guard improves the utility-privacy trade-off by 15.8% and reduces computational overhead by 23.4%. Key contributions include adaptive noise injection, dynamic privacy budgeting, and an integrated privacy auditing module—collectively advancing secure and trustworthy AI deployment in clinical environments.