We have 3 of 9 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
VIF-Bench is introduced, a benchmark of 1,241 tasks designed to assess the edge of model capabilities in this joint setting in which multiple references must be composed under multiple and heterogeneous visual-instruction images, and uncover three findings.
This work proposes AutoRef, which optimizes the harness automatically while keeping both models frozen: a coding agent iteratively rewrites the harness code, and discovers AutoRef-Harness, which improves the open-weight FLUX.
This work formalizes target-mode coverage as target-mode coverage, the coverage of a predefined set of semantically specified modes, and proposes multi-axis max@K, a group-based reinforcement learning objective for improving it in diffusion-based T2I models.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
