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Research Editor selected

GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving

GuidedWAM is a model-agnostic framework that injects driving objectives into frozen world action models (WAMs) at sampling time, avoiding retraining. It uses differentiable energies on the sampler's intermediate clean-action estimate, stabilized by scheduling, normalization, and velocity-bounded correction. On NAVSIM v2 hard-split, it improves three frozen WAMs by 1.8 to 3.6 EPDMS with less than 10% added sampling time per step.

Why it matters: This paper presents a practical post-training control method for autonomous driving models, offering a training-free alternative to retraining, which is valuable for researchers and practitioners.

arxiv13h agoScore 83

autonomous drivinggenerative modelsmodel guidance

Research

CANDO: Conservative Action-Noise Directional Optimization for Residual Reinforcement Learning

This paper introduces CANDO, a residual reinforcement learning method that improves frozen generative policies by combining noise-space and action-space residual optimization. CANDO removes fixed latent feasible regions and uses conservative value estimation with gated execution to regulate adaptation. Experiments show CANDO achieves 76.1 NAUC and 91.4% final success rate, surpassing single-space baselines by 6.6 NAUC and 4.1%.

arxiv2d agoScore 78

Reinforcement LearningRobot ControlPolicy Adaptation

Research Editor selected

AegisFlow Proposes Training-Free Guidance for Safer Generative Trajectories

AegisFlow is a training-free framework designed to guide pretrained generative models toward target outcomes while maintaining constraints throughout generation. It formulates guidance as a global optimal-control problem, incorporating a time-varying barrier function and a tractable approximation of the Hamilton–Jacobi–Bellman equation. The authors report improved trade-offs between target alignment and constraint satisfaction in text-guided image manipulation and robotic planning, and provide a code repository.

Why it matters: The research offers a concrete approach to constraint-aware generation, with reported experiments spanning image manipulation and robotic planning and code available for evaluation.

arxivOct 30, 2026Score 82

AegisFlowGenerative modelsConstraint-aware generation