23 citations · 27 across the 9 of their papers we have counts for
11 papers
VIEWCRAFT: Evolving What Multimodal Agents See and How They Read It
Binxiao Xu, Ruichuan An, Bocheng Zou +7
Reusable skills improve multimodal agents without updating model weights, but their effectiveness depends on both the evidence presented and the procedure used to read it. We intro…
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
Zhekai Chen, Chengqi Duan, Kaiyue Sun +4
The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assist…
CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images
Chengqi Duan, Kaiyue Sun, Rongyao Fang +10
Recent advances in Large Language Models (LLMs) and Vision Language Models (VLMs) have shown significant progress in mathematical reasoning, yet they still face a critical bottlene…
SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Weiyang Jin, Yuwei Niu, Jiaqi Liao +4
Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding capabilities within a single framework…
FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmark
Rongyao Fang, Aldrich Yu, Chengqi Duan +7
The advancement of open-source text-to-image (T2I) models has been hindered by the absence of large-scale, reasoning-focused datasets and comprehensive evaluation benchmarks, resul…
T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation
Kaiyue Sun, Rongyao Fang, Chengqi Duan +2
We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design,…
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning
Chengqi Duan, Rongyao Fang, Yuqing Wang +5
Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that specify multiple objects with precise…
GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing
Rongyao Fang, Chengqi Duan, Kun Wang +9
Current image generation and editing methods primarily process textual prompts as direct inputs without reasoning about visual composition and explicit operations. We present Gener…
PUMA: Empowering Unified MLLM with Multi-granular Visual Generation
Rongyao Fang, Chengqi Duan, Kun Wang +7
Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimo…
T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation
Kaiyi Huang, Chengqi Duan, Kaiyue Sun +3
Despite the impressive advances in text-to-image models, they often struggle to effectively compose complex scenes with multiple objects, displaying various attributes and relation…
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang +4
Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training.…