PiL-World introduces a chunk-wise world model for closed-loop VLA policy evaluation that reduces the gap between simulated and real success rates from 63.2% to 12.0% on three dual-arm manipulation tasks by conditioning on action-derived visual control and latent histories while training on both succ
Avid: Adapting video diffusion models to world models
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EA-WM generates more accurate robot world rollouts by projecting actions as structured visual fields in camera space and using event-aware bidirectional fusion to better capture interaction dynamics.
EO-WM is a diffusion transformer that adds physically separated baseline-anomaly and cumulative-stress conditioning to probabilistic EO forecasting and validates it on two new weather-response benchmarks, reporting 5.63% and 7.80% relative gains on NDVI decline metrics.
IOI decouples deterministic kinematics from stochastic physics in interactive world models by rendering forward-kinematics trajectories into multi-view projections that guide a video generator, achieving SOTA fidelity and OOD generalization on RoboTwin.
Prisma-World is a diffusion-based multi-agent video model that uses joint full-attention, multi-agent RoPE, and relative camera geometry injection plus curriculum training to produce consistent cross-view videos from flexible agent counts.
Introduces mesh tokenization to condition DiT-based video diffusion models directly on 3D human meshes for motion control without 2D rendering.
Embodied AI requires query-conditioned world models that select the simplest physical abstraction sufficient to answer intervention queries.
OrbiSim builds a differentiable physics engine from world models to support gradient-based policy optimization and contact modeling in robotics.
citing papers explorer
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PiL-World: A Chunk-Wise World Model for VLA Policy-in-the-Loop Evaluation
PiL-World introduces a chunk-wise world model for closed-loop VLA policy evaluation that reduces the gap between simulated and real success rates from 63.2% to 12.0% on three dual-arm manipulation tasks by conditioning on action-derived visual control and latent histories while training on both succ
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EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
EA-WM generates more accurate robot world rollouts by projecting actions as structured visual fields in camera space and using event-aware bidirectional fusion to better capture interaction dynamics.
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EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
EO-WM is a diffusion transformer that adds physically separated baseline-anomaly and cumulative-stress conditioning to probabilistic EO forecasting and validates it on two new weather-response benchmarks, reporting 5.63% and 7.80% relative gains on NDVI decline metrics.
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IOI: Decoupling Kinematics and Physics for Interactive World Models
IOI decouples deterministic kinematics from stochastic physics in interactive world models by rendering forward-kinematics trajectories into multi-view projections that guide a video generator, achieving SOTA fidelity and OOD generalization on RoboTwin.
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Prisma-World: Camera-Controllable Multi-Agent Video World Model
Prisma-World is a diffusion-based multi-agent video model that uses joint full-attention, multi-agent RoPE, and relative camera geometry injection plus curriculum training to produce consistent cross-view videos from flexible agent counts.
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Towards 3D-Aware Video Diffusion Models: Render-Free Human Motion Control with Mesh Tokenization
Introduces mesh tokenization to condition DiT-based video diffusion models directly on 3D human meshes for motion control without 2D rendering.
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Physically Viable World Models: A Case for Query-Conditioned Embodied AI
Embodied AI requires query-conditioned world models that select the simplest physical abstraction sufficient to answer intervention queries.
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OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence
OrbiSim builds a differentiable physics engine from world models to support gradient-based policy optimization and contact modeling in robotics.