5 papers · 1 filter
Value Gradient Guidance for Flow Matching Alignment
Zhen Liu, Tim Z. Xiao, Carles Domingo-Enrich +2
While methods exist for aligning flow matching models--a popular and effective class of generative models--with human preferences, existing approaches fail to achieve both adaptati…
AutoNeural: Co-Designing Vision-Language Models for NPU Inference
Wei Chen, Liangmin Wu, Yunhai Hu +9
While Neural Processing Units (NPUs) offer high theoretical efficiency for edge AI, state-of-the-art Vision--Language Models (VLMs) tailored for GPUs often falter on these substrat…
Compositional Machine Design as Program Synthesis with LLMs
Wenqian Zhang, Yangyi Huang, Weiyang Liu +1
Large language models (LLMs) have shown strong abilities in writing and revising programs, yet many program-synthesis benchmarks still evaluate programs in symbolic or digital envi…
Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards
Qingming Liu, Zhen Liu, Dinghuai Zhang +1
Generating high-quality and photorealistic 3D assets remains a longstanding challenge in 3D vision and computer graphics. Although state-of-the-art generative models, such as diffu…
Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling
Tim Z. Xiao, Johannes Zenn, Zhen Liu +3
Large language models (LLMs) can often accurately describe probability distributions using natural language, yet they still struggle to generate faithful samples from them. This mi…