5 papers
PRISMA: Reinforcement Learning Guided Two-Stage Policy Optimization in Multi-Agent Architecture for Open-Domain Multi-Hop Question Answering
Yu Liu, Wenxiao Zhang, Cong Cao +10
Answering real-world open-domain multi-hop questions over massive corpora is a critical challenge in Retrieval-Augmented Generation (RAG) systems. Recent research employs reinforce…
Emotion-Director: Bridging Affective Shortcut in Emotion-Oriented Image Generation
Guoli Jia, Junyao Hu, Xinwei Long +5
Image generation based on diffusion models has demonstrated impressive capability, motivating exploration into diverse and specialized applications. Owing to the importance of emot…
Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling
Zhiyuan Ma, Ruixun Liu, Sixian Liu +2
Recently, the rectified flow (RF) has emerged as the new state-of-the-art among flow-based diffusion models due to its high efficiency advantage in straight path sampling, especial…
Context-Aware Autoregressive Models for Multi-Conditional Image Generation
Yixiao Chen, Zhiyuan Ma, Guoli Jia +3
Autoregressive transformers have recently shown impressive image generation quality and efficiency on par with state-of-the-art diffusion models. Unlike diffusion architectures, au…
Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines
Xinwei Long, Zhiyuan Ma, Ermo Hua +3
Retrieval-augmented generation (RAG) has emerged to address the knowledge-intensive visual question answering (VQA) task. Current methods mainly employ separate retrieval and gener…