We introduce Mixture-of-Trees (MoT), a novel framework that integrates sparse expert activation with structured tree-based reasoning for efficient LLM inference. MoT employs a learned gating mechanism to selectively activate only the most relevant expert reasoning trees for each problem, where experts use models of varying capacities based on task complexity. The framework features three key innovations: (1) sparse expert activation through unified gating networks, (2) specialized expert trees that leverage domain-specific expertise while optimizing the quality-efficiency trade-off, and (3) collaborative debate mechanisms for conflicting solutions. Additionally, MoT includes a shared baseline tree with early stopping—activated experts perform lightweight validation and terminate early when confidence is high. Experiments across five benchmarks (GSM8K, MATH, AIME 2024, MMLU, HotpotQA) show that MoT achieves 2-7 percentage point accuracy improvements while reducing LLM calls by 37-40% compared to existing multi-path methods.

Artificial intelligence has demonstrated significant potential in clinical decision-making; however, developing models capable of adapting to diverse real-world scenarios and performing complex diagnostic reasoning remains a major challenge. Existing medical multi-modal benchmarks are typically limited to single-image, single-turn tasks, lacking multi-modal medical image integration and failing to capture the longitudinal and multi-modal interactive nature inherent to clinical practice. To address this gap, we introduce MedAtlas, a novel benchmark framework designed to evaluate large language models on realistic medical reasoning tasks. MedAtlas is characterized by four key features: multi-round visual question answering (VQA), Joint reasoning of multiple modalities of medical images, multi-task integration, and high clinical fidelity. It supports four core tasks: open-ended multi-round VQA, closed-ended multi-round VQA, multi-image joint reasoning, and comprehensive disease diagnosis. Each case is derived from real diagnostic workflows and incorporates temporal interactions between textual medical histories and multiple imaging modalities, including CT, MRI, PET, ultrasound, X-ray, etc., requiring models to perform deep integrative reasoning across images and clinical texts. MedAtlas provides expert-annotated gold standards for all tasks. Furthermore, we propose two novel evaluation metrics: Stage Chain Accuracy (SCA) and Error Propagation Suppression Coefficient (EPSC). Benchmark results with existing multi-modal models reveal substantial performance gaps in multi-stage clinical reasoning. MedAtlas establishes a challenging evaluation platform to advance the development of robust and trustworthy medical AI.

Large Language Models (LLMs) are achieving unprecedented performance across diverse tasks, benefiting from autoregressive generation. However, this left-to-right decoding paradigm inherently limits contextual understanding quality. Diffusion-based LLMs (dLLMs) offer a promising alternative by iteratively refining sequences via denoising, enabling stronger bidirectional context modeling and improved generation quality. However, dLLMs face two main challenges: redundant computation and memory overhead in multi-step denoising, and excessive inference cost from over-denoising under fixed-step schedules. To address these issues, we propose dLLM-OPU, an FPGA overlay processor to accelerate dLLMs. Our solution features two key innovations: (1) a Region-Adaptive Caching for Dynamic Column Sparsity Framework that exploits temporal locality for selective recomputation without model retraining, and (2) a Token Entropy-based Early Stopping strategy that dynamically terminates the denoising process based on token-level convergence metrics. We implement these innovations through a specialized sparse processing element (PE) array that maximizes top-k sparsity utilization by minimizing idle cycles via row-column concatenation, complemented by an efficient cache management system that reduces memory access latency and a flexible entropybased decoding unit. Implemented on a U200 FPGA, dLLM-OPU achieves $2.2 \times-5.1 \times$ speedup and $7.6 \times-20.3 \times$ energy efficiency over RTX4090 in LLaDA.

Landmark detection is essential in medical image analysis, aiding tasks like surgical navigation, diagnosis, and treatment planning. However, it remains challenging due to the need for fine-grained local detail and long-range spatial dependency modeling in high-dimensional volumetric data. Existing approaches struggle to balance accuracy, efficiency, and robustness, especially in cases of sparse landmark distribution, anatomical variability, and noisy or incomplete scans. We propose H3DE-Net, a hybrid framework combining CNNs for local feature extraction and a lightweight transformer-based attention module for global context. A volumetric bi-level routing attention mechanism reduces computational overhead while preserving long-range dependencies, and multi-scale feature fusion enhances precision and robustness. This design integrates both global and local representations, overcoming the limitations of CNN-only or transformer-only models. Extensive experiments on a public CT dataset show that H3DE-Net achieves state-of-the-art performance, significantly improving mean radial error (MRE) and success detection rate (SDR) compared to existing methods. The model is robust in challenging scenarios with missing landmarks or anatomical variations, demonstrating its applicability in real-world clinical settings. All code, pretrained weights, and data processing scripts are publicly available for reproducibility and further research.

The advent of Large Language Models (LLMs) like DeepSeek, empowered by the Mixture-of-Experts (MoE) architecture, has driven significant advancements across diverse applications. However, a critical challenge arises during inference: Only a small fraction of experts are activated, causing severe token allocation imbalances among experts. This inefficiency poses substantial storage and computational burdens on resource-constrained devices, exacerbated by the lack of optimization strategies that integrate expert usage-aware parameter pruning and parallel scheduling, ultimately leading to suboptimal resource utilization. To address these limitations, we propose MoE-OPU, an FPGA-based overlay processor that optimizes parallel MoE inference through three key innovations. First, we introduce N:M sparsity (1:4/2:4/4:8/6:8/8:8) in the MLP layers and mixed-precision quantization (BF16/FP8/INT4) guided by expert activation frequency, reducing the parameter size by up to 2.76× while maintaining model accuracy (only 1.53% average drop after fine-tuning). Second, a lightweight prediction network dynamically predicts next-layer "hot" experts by analyzing historical activation patterns and current hidden states, achieving an average prediction hit rate of 83.4%. Third, a reconfigurable multi-core architecture maximizes the utilization of HBM bandwidth via a systolic array that natively supports sparse and mixed-precision computations, coupled with parallel concatenation to balance compute and memory efficiency. Experimental results on a Xilinx V80 FPGA with the DeepSeek-V2-lite model demonstrate that MoE-OPU outperforms the NVIDIA A100 GPU, delivering a 6.78× higher token throughput. Compared to RTX 4090 and U200 FPGA, MoE-OPU achieves 13.37× and 7.85× improvements, respectively. These advancements highlight the potential of algorithm-hardware co-design for scalable deployment of MoE-based LLMs on edge devices.

Device models require large numbers of parameters to characterize complex physical effects. Although the latest advancements in machine learning and automated tools have drastically improved efficiency over the classic methods, they still demand a considerable amount of human intervention in the loop to gain accuracy. This drastically limits further automation. Inspired by the success of Multimodal Large Language Models (MLLMs) in addressing tasks across diverse fields, we propose ModelGen, the first in-depth study to leverage MLLMs with RAG (Retrieval-Augmented Generation) to significantly reduce human effort in parameter extraction for compact model. Our contributions include (1) Automated Agentic Workflow Construction that learns to build and refine extraction workflows through iterative optimization, (2) MLLM Judge, a visual scoring mechanism that evaluates fitting quality using actual device characteristic plots rather than simple numerical metrics, and (3) Model-specific RAG for providing relevant domain knowledge during the extraction process. Experimental results demonstrate that ModelGen achieves a 26.8%–33.1% improvement in pass@1,3,5 compared to base LLM methods. The system completes complex model extractions for BSIMs and ASM-HEMT in hours (up to 168× faster) rather than days or weeks, making parameter extraction more accessible to non-experts while maintaining professional engineer-level accuracy.