This work presents a novel trie (prefix-tree)based parallel decoding method that addresses the memory inefficiency of batch-based beam search.By sharing a single KV cache across beams with common prefixes, our approach dramatically reduces memory usage and enables efficient decoding.We evaluated our method across three attention architectures, Multi-Head Attention (Phi-3.5-miniinstruct),Grouped Query Attention (Llama-3.1-8B-Instruct),and Sliding Window Attention (Mistral-Small-24B-Instruct-2501), using CN-N/DailyMail for abstractive summarization and HumanEval for code generation.Our experiments demonstrate substantial memory savings (4-8×) and up to 2.4× faster decoding, without compromising generation quality.These results highlight our method's suitability for memory-constrained environments and largescale deployments.

Stable Diffusion models have made remarkable strides in generating photorealistic images from text prompts but often falter when tasked with accurately representing complex spatial arrangements, particularly involving intricate 3D relationships. To address this limitation, we introduce SmartSpatial, an innovative approach that not only enhances the spatial arrangement capabilities of Stable Diffusion but also fosters AI-assisted creative workflows through 3D-aware conditioning and attention-guided mechanisms. SmartSpatial incorporates depth information injection and cross-attention control to ensure precise object placement, delivering notable improvements in spatial accuracy metrics. In conjunction with SmartSpatial, we present SmartSpatialEval, a comprehensive evaluation framework that bridges computational spatial accuracy with qualitative artistic assessments. Experimental results show that SmartSpatial significantly outperforms existing methods, setting new benchmarks for spatial fidelity in AI-driven art and creativity.