large language models

An Explainable Multimodal Deep Learning Framework for Alzheimer's Disease Classification Using MRI, PET, and Clinical Data

Abstract Alzheimer's disease (AD) often remains undetected until memory loss and cognitive decline become clinically apparent, at which point substantial neurological damage has already occurred. Early and reliable diagnosis therefore, remains one of the most pressing challenges in dementia care. This study presents a...

Exploring 3D point clouds with multimodal large language model (MLLM): A review

Knowledge graph-enhanced large language models for decision support in coastal waterway aids to navigation

Multi-grained image-text retrieval in farmland remote sensing via multimodal large language models

Dual-stream representation learning with large language models for electricity demand forecasting

Designing Growth Mindset-Inspired Feedback Messages Tailored to Learning Behaviors Using LLM and Persuasive Strategies

This work introduces a four-part feedback message structure inspired by the growth mindset principle, which encourages students to reflect, persist, and embrace challenges, and provides an initial foundation for explainable, rule-based feedback generation that can be extended through experimentation with learners.

CrossServe: Cross-Layer Scheduling for SLO Optimization in Multi-Tenant LLM Serving

The deployment of Large Language Models (LLMs) as multi-tenant cloud services is now widespread, but maintaining high Service Level Objective (SLO) attainment across diverse tenants remains challenging. Current serving systems focus on a single layer of the stack, either using iteration-level batching or coarse-grained...

Harmonic Intelligence: Recursive Analytic Meta-Geometry, Residual-Bearing Learning, and the Geometry of Solution Types — Integrated Edition

[wAI ~ wErrors] «All models are wrong, but some are useful.» — George E. P. Box (1976) This preprint synthesizes a large exploratory notebook on metric atlases, spectral and harmonic representations, residual-bearing learning, recursive joint/meta-diagonalization, Galois and monodromy fingerprints, sheaf/cohomological...

Online Scheduling of Battery-Aware Speculative Decoding for Energy-Efficient Cloud-Edge Collaborative LLM Inference

This work designs a learning-centric algorithmic framework which employs bandit learning to address the intractability and manage draft length selection in the first subproblem and applies online learning to address the online uncertainty and manage battery states and grid-energy use in the second subproblem, both with...

GroupKV: Hierarchical KV Cache Management for Long-Context Diffusion LLM Inference

Diffusion large language models (dLLMs) are emerging as a promising generative paradigm that complements autoregressive decoding. In long-context settings, KV cache bloat and offloading transfer overhead have become primary bottlenecks in inference systems. Meanwhile, the periodic full-sequence recomputation and locali...

BR-MoE: A Memory-Budget-Aware Co-Design Framework for Efficient MoE Inference on Resource-Constrained GPUs

The Mixture-of-Experts (MoE) architecture has become the dominant paradigm for scaling Large Language Models, yet its explosive parameter growth routinely overflows the GPU memory budget. Weight quantization offers a practical way to fit these models into limited GPU memory, but reducing precision below 4 bits causes s...

OmniPipe: Efficient, Flexible and Scalable Pipeline Parallelism for Large Model Training

Mixture-of-Experts (MoE) has become the de facto architecture for scaling large language models, offering expanded capacity with manageable compute. Pipeline parallelism (PP) is indispensable for distributed MoE training, but state-of-the-art PP schemes face three major limitations: large pipeline bubbles, high per-sta...