GenED-SC: Generative Editing Semantic Communication with Integrated Multi-Modal LLMs

Deep learning-based joint source-channel coding has recently demonstrated strong potential for semantic communication (SemComm). However, most existing approaches focus on optimizing visual-fidelity metrics, which can lead to reduced perceptual quality. Generative model-based SemComm leverages rich prior knowledge from large-scale pre-training to enhance perceptual quality, but often at the cost of increased distortion and unreliability. This paper addresses the above issues by proposing a two-stage semantic image transmission framework, integrating a multimodal large language model (MLLM) for generative editing. In the first stage, a JSCC-based discriminative transmission selectively prioritizes semantically important regions, preserving scene layout and object integrity under limited bandwidth. In the second phase, MLLM-driven generative editing refines missing details based on the textual descriptions, enhancing semantic fidelity and perceptual quality. Extensive experiments show that the proposed framework achieves state-of-the-art performance in semantic preservation, perceptual quality, and visual fidelity across a wide range of channel conditions, especially in low-SNR regimes.

Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree

Striking an optimal balance between minimal drafting latency and high speculation accuracy to enhance the inference speed of Large Language Models remains a significant challenge in speculative decoding. In this paper, we introduce Falcon, an innovative semi-autoregressive speculative decoding framework fashioned to augment both the drafter's parallelism and output quality. Falcon incorporates the Coupled Sequential Glancing Distillation technique, which fortifies inter-token dependencies within the same block, leading to increased speculation accuracy. We offer a comprehensive theoretical analysis to illuminate the underlying mechanisms. Additionally, we introduce a Custom-Designed Decoding Tree, which permits the drafter to generate multiple tokens in a single forward pass and accommodates multiple forward passes as needed, thereby boosting the number of drafted tokens and significantly improving the overall acceptance rate. Comprehensive evaluations on benchmark datasets such as MT-Bench, HumanEval, and GSM8K demonstrate Falcon's superior acceleration capabilities. The framework achieves a lossless speedup ratio ranging from 2.91x to 3.51x when tested on the Vicuna and LLaMA2-Chat model series. These results outstrip existing speculative decoding methods for LLMs, including Eagle, Medusa, Lookahead, SPS, and PLD, while maintaining a compact drafter architecture equivalent to merely two Transformer layers.