Shanghai Artificial Intelligence Laboratory

Multi-modal parameter-efficient fine-tuning via hypergraph adapters

Parameter-efficient fine-tuning (PEFT) is an attractive strategy for adapting vision–language models when compute, memory, or labeled data are limited. However, most multimodal PEFT methods still operate on individual samples or pairwise relations, which leaves higher-order struc...

NeuroChrono: Dissociated Neural Encoding with ChronoGate for Brain-to-Video Diffusion

Reconstructing natural video viewed by a subject from non-invasive neural recordings is a challenging problem at the intersection of computational neuroscience and generative modelling. Existing brain-to-video methods encode multi-modal neural signals (typically fMRI together wit...

Interpretable multi-modal hierarchical framework to support cytomorphological analysis of hematologic cancers

Cytomorphological analysis of bone marrow aspirate (BMA) and peripheral blood smears (PBS) remains a diagnostic cornerstone in hematology. Machine-assisted analysis could support diagnostic workflows, but remains challenging due to the imbalanced prevalence of hematologic maligna...

Language-enhanced generative modeling for amyloid PET synthesis from MRI and blood biomarkers.

Assessment of amyloid pathology in Alzheimer's disease (AD) often relies on amyloid-beta positron emission tomography (Aβ-PET), but its clinical use is limited by cost and accessibility. We developed a language-enhanced generative framework to synthesize Aβ-PET images from T1-wei...

Diff-VF: Training-free High-quality Long Video Generation via Diffusion Model

Recently, diffusion models have made great progress in video generation. However, most existing video diffusion models are trained with short videos, and degrade when extrapolated to long videos, struggling to maintain long-range temporal coherence while retaining diverse motions...

Large language models as designers for autonomous research of anisotropic polymer thermocells

Flexible thermoelectric materials face a persistent trade-off between output power and mechanical robustness. Here, we report OmniMat, a large language model-driven framework that designs experimental protocols in a human-in-the-loop workflow without relying on structured task-sp...