6 papers
EPIC: Abstraction and Polymorphism of In-Network Collectives on Ethernet
Yitao Yuan, Jianglong Nie, Tianyu Bai +28
In-Network Collective (INC) acceleration holds immense potential for optimizing AI training and inference; however, its cross-layer nature has historically hindered investment and…
Scaling Multi-Reference Image Generation with Dynamic Reward Optimization
Wenwang Huang, Yusen Fu, Junjie Wang +6
While personalized image generation has achieved remarkable progress, multi-reference image generation (MRIG) remains a challenging task. Most existing benchmarks fail to adequatel…
Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation
Junjie Wang, Xinghua Lou, Jason Li +8
Text-to-Image (T2I) models and Unified Multimodal Models (UMMs) have achieved remarkable progress in visual generation. However, their reliance on a single-pass generation paradigm…
AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech
Bin Kang, Shaoguo Wen, Yang Fan +6
While existing text-to-speech (TTS) models exhibit high expressiveness, fine-grained control over composite instructions remains challenging due to the structural mismatch between…
DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
Boheng Li, Junjie Wang, Yiming Li +7
Despite the integration of safety alignment and external filters, text-to-image (T2I) generative systems are still susceptible to producing harmful content, such as sexual or viole…
Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuning
Boheng Li, Renjie Gu, Junjie Wang +5
Text-to-image (T2I) diffusion models have achieved impressive image generation quality and are increasingly fine-tuned for personalized applications. However, these models often in…