Mingyan Li

SG-Layout: Structured Scene Graph-Guided Layout Generation with LLMs

Understanding and generating spatially coherent layouts from natural language remains a fundamental yet challenging task for large language models (LLMs). Existing LLMs often struggle to capture explicit geometric relationships and structural dependencies between objects. To address this issue, we propose SG-Layout, a graph-guided layout generation framework that explicitly incorporates structured spatial knowledge into LLMs. SG-Layout follows a two-stage training paradigm: (1) a graph-language feature alignment stage, where a relational graph encoder and a projector are trained to map scene-graph embeddings into the LLM's linguistic space; and (2) an instruction tuning stage, where LoRA-based adapters enable efficient fine-tuning for instruction-driven layout generation while keeping the backbone frozen. We evaluate SG-Layout on image layout generation, indoor scene synthesis and robotic object rearrangement tasks. Experimental results show that SG-Layout improves spatial reasoning accuracy and geometric consistency over the compact open-source backbone, with particularly clear advantages in relation-dense and compositionally complex scenes. These results highlight the effectiveness of graph-structured feature alignment for enhancing controllable layout generation.

EdgeCoInfer: Hierarchical Collaborative Inference for On-Device Multimodal Large Models

To deliver ubiquitous intelligence, modern mobile applications increasingly execute concurrent Multimodal Large Language Models (MLLMs) on edge devices, presenting severe challenges under multi-task concurrency and tight resource constraints. To address this, we propose EdgeCoInfer, a hierarchical collaborative inference framework enabling efficient on-device MLLM inference through coarse-to-fine orchestration. Coarsely, EdgeCoInfer decomposes MLLMs into functional modules for inter-task sharing, avoiding redundant model loading. Finely, it partitions models at the neural network layer level and distributes segments across devices and servers. We jointly optimize layer partitioning, module sharing, and resource allocation under tight constraints. To tackle the non-differentiable combinatorial explosion, we propose a Hybrid Evolutionary Hierarchical Reinforcement Learning (HE-HRL) framework. HE-HRL synchronizes a gradient-free genetic algorithm for discrete partitioning and sharing decisions with a gradient-based soft actor-critic agent for continuous resource refinement. We further embed a constructive cut-step decoder with pre-act pruning and a two-phase curriculum to improve feasibility and accelerate convergence. Experimental results show that EdgeCoInfer breaks the edge memory wall and prevents catastrophic out-of-memory and task failures under high concurrency, reducing memory demand by 53.53\% and system cost by 59.86\% compared to existing methods.

Disturbance-Aware Hybrid Learning for Robust and Adaptive UAV Flight in Extreme Winds