1 citations · 1 across the 8 of their papers we have counts for
9 papers
UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations
Zihan Ding, Longxu Dou, Qi Gao +26
Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution. Routine workfl…
Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning
Peng Xia, Kaide Zeng, Jiaqi Liu +5
Large Language Model (LLM) Agents, often trained with Reinforcement Learning (RL), are constrained by a dependency on human-curated data, limiting scalability and tethering AI to h…
BLIP3o-NEXT: Next Frontier of Native Image Generation
Jiuhai Chen, Le Xue, Zhiyang Xu +12
We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3 series that advances the next frontier of native image generation. BLIP3o-NEXT unifies text-to-image gener…
CoDA: Coding LM via Diffusion Adaptation
Haolin Chen, Shiyu Wang, Can Qin +12
Diffusion language models promise bidirectional context and infilling capabilities that autoregressive coders lack, yet practical systems remain heavyweight. We introduce CoDA, a 1…
Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training
Tianqing Fang, Zhisong Zhang, Xiaoyang Wang +16
General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reasoning, web interaction, coding, an…
VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents
Rui Meng, Ziyan Jiang, Ye Liu +10
Multimodal embedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering over different modalities. How…
BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
Jiuhai Chen, Zhiyang Xu, Xichen Pan +10
Unifying image understanding and generation has gained growing attention in recent research on multimodal models. Although design choices for image understanding have been extensiv…
Could AI Trace and Explain the Origins of AI-Generated Images and Text?
Hongchao Fang, Yixin Liu, Jiangshu Du +7
AI-generated content is becoming increasingly prevalent in the real world, leading to serious ethical and societal concerns. For instance, adversaries might exploit large multimoda…
MuseumMaker: Continual Style Customization without Catastrophic Forgetting
Chenxi Liu, Gan Sun, Wenqi Liang +3
Pre-trained large text-to-image (T2I) models with an appropriate text prompt has attracted growing interests in customized images generation field. However, catastrophic forgetting…