arXiv AI By Jihoon Hong, Alice Chan, Qiyue Dai, Julian Skifstad, Glen Chou

Activation Steering of Video Generation Models via Reduced-Order Linear Optimal Control

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arXiv:2606. 04775v1 Announce Type: cross Abstract: Text-to-video (T2V) models trained on large-scale web data can generate undesired content, motivating interventions that reduce harmful outputs without sacrificing visual quality.

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arXiv AI
4d ago

CoRe: Co-Evolving Reward Models for Mitigating Latent Reward Hacking in Video Diffusion Models

CoRe introduces a co‑evolving reward framework to mitigate latent reward hacking in video diffusion models. By continuously refitting the latent‑reward model on the generator’s current samples and anchoring it to real‑video preferences, CoRe prevents the generator from drifting outside the reward model’s training support. Experiments on Wan2.1‑T2V‑1.3B demonstrate that CoRe improves generation quality over pretrained models and prior alignment methods while avoiding quality collapse.

By Zhaolong Su, Yujin Han, Feng Wang, Jameson Dong, Hins Hu, Difan Zou
arXiv AI
Jun 12

ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding

arXiv:2506. 01274v2 Announce Type: replace-cross Abstract: Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to video understanding remains constrained by suboptimal frame selection strategies, albeit with the rapid development of video-specialized LMMs.

By Hosu Lee, Junho Kim, Hyunjun Kim, Yong Man Ro