HOI-Edit benchmarks human–object interaction image editing with a VLM-based metric, and SCPE steers I2V models via agentic self-correcting prompts to produce competitive HOI edits.
arXiv preprint arXiv:2308.07234 (2023)
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PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
SparseWorld is a sparse world model with a Sparse Dreamer module that performs autoregressive rollout of future instances to refine motion prediction and planning, reporting 0.05% collision rate on nuScenes open-loop metrics.
Dense future RGB/depth world modeling both supervises a VLA planner and supplies safety-gated uncertainty rewards that, optimized with GRPO, reach 93.7 PDMS on NAVSIM.
The paper delivers a multi-axis taxonomy for world models that maps architectures, training families, reasoning strategies, and domains from early cognitive foundations through systems such as Dreamer, MuZero, and Sora while noting evaluation gaps.
citing papers explorer
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Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework
HOI-Edit benchmarks human–object interaction image editing with a VLM-based metric, and SCPE steers I2V models via agentic self-correcting prompts to produce competitive HOI edits.
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Physically Interpretable World Models via Weakly Supervised Representation Learning
PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation
SparseWorld is a sparse world model with a Sparse Dreamer module that performs autoregressive rollout of future instances to refine motion prediction and planning, reporting 0.05% collision rate on nuScenes open-loop metrics.
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ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving
Dense future RGB/depth world modeling both supervises a VLA planner and supplies safety-gated uncertainty rewards that, optimized with GRPO, reach 93.7 PDMS on NAVSIM.
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World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
The paper delivers a multi-axis taxonomy for world models that maps architectures, training families, reasoning strategies, and domains from early cognitive foundations through systems such as Dreamer, MuZero, and Sora while noting evaluation gaps.