…AI-generated summary While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video generation methods based on diffusion models…
…An Advanced World Action Model for Robot Control (2026) NoiseGate: Learning Per-Latent Timestep Schedules as Information Gating in World Action Models (2026) Privileged Foresight Distillation: Zero-Cost Future Correction for World…
…against compute, memory, latency, and action-label cost. The field is moving toward methods that generate less of the future while preserving what control requires. The survey homepage is available at https…
…AI-generated summary Vision-Language-Action (VLA) models achieve remarkable flexibility and generalization beyond classical control paradigms. However, most prevailing VLAs are trained under a single-frame observation paradigm, which leaves them…
…With a minimal BoS anchor, this simple change preserves broad benchmark quality while reducing long-context cost. In a controlled Llama-3.1-8B instruction-tuning study, SPEED using only 75\% of…
…yielding a difficult-to-control mechanism that can vary across safety-relevant scenarios. At the same time, adapting model behavior through full fine-tuning or retraining is costly, especially when developers need…
…Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost Published on Apr 28 Submitted by Venkata Pushpak Teja Menta on…
…Acquiring new data is costly and ethically constrained, motivating generative data augmentation . Current generative methods are often based on latent diffusion models , which operate in learned low dimensional latent spaces to address…
…We further propose an amortized formulation that exploits the affine structure of the loss used in DiffusionNFT, to reduce the per-step cost from K+1 backward passes to near single-reward…
…online reinforcement learning is able to effectively address multi-turn dynamics but is prohibitively expensive due to the cost of generating full correction trajectories at every update, whereas offline supervised fine-tuning…