arXiv:2605.10302v4 Announce Type: replace
Abstract: Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching a...
By Pedro M. P. Curvo, Maksim Zhdanov, Floor Eijkelboom, Jan-Willem van de Meent
VideoAWR introduces a decoupled-timestep advantage‑weighted rectification framework for video flow models, separating guided rollouts from the optimization anchor to avoid geometric guidance blow‑up and cold‑start collapse. The method uses an EMA reference policy for guided rollouts while rectifying against an unguided conditional anchor, proving that the centered advantage cancels the anchor in expectation and retains only reward‑correlated covariance. Experiments on Wan2.1‑1.3B and 14B models show stable training and improved visual and motion quality in both CFG‑free and CFG‑enabled settings.
By Zheming Fu, Wenbin Ning, Ruizhe He, Wei Shang, Xiaoxiao Ma, Lianrui Mu, Chang Liu, Lei Wang, Siming Fu
arXiv:2605.18163v2 Announce Type: replace
Abstract: Hallucination correction is not a one-direction problem. We show that intermediate layers are neither uniformly more truthful than final layers nor...
By Tej Sanibh Ranade
arXiv:2607. 06114v1 Announce Type: cross Abstract: Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs).
By Xin Peng, Ang Gao
PROOF-Gen is a method that improves distillation of tool‑calling models by recovering successful trajectories from teacher failures. It uses per‑scenario prompt optimization to generate corrective guidance that steers the teacher to a passing trajectory, then removes this guidance before training so the student learns from clean demonstrations. On τ2‑bench, PROOF-Gen recovers 93% of failed scenarios, boosting Qwen3‑4B‑Instruct‑2507’s Pass^1 from 0.132 to 0.529 and improving Gemma 4 E4B‑it by 7.2pp on BFCL v4 multi‑turn, while also raising deployed on‑device model performance by up to 5.0pp across response‑quality metrics.
By Anh Ta, Junjie Zhu, Shahin Shayandeh
arXiv:2609.35767v2 Announce Type: replace-cross
Abstract: Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image g...
By Yijia Fan, Ziqi Huang, Zhongang Cai, Yan Li, Zimo Wen, Wanqi Yin, Haiwen Diao, Ziwei Liu