MemLearner introduces a learning-based adaptive context query method using query tokens in video world models to improve long-term scene consistency over rule-based retrieval.
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Irasim: A fine-grained world model for robot manipulation
23 Pith papers cite this work. Polarity classification is still indexing.
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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
MiraBench defines action-conditioned reliability via three levels (physics adherence, action-following fidelity, optimism bias detection) and applies it to 12 model configurations using a 16,000-judgment human corpus, finding visual fidelity a poor proxy for action fidelity, no reliable scale benefi
WBench is a benchmark with 289 test cases and 1,058 turns for evaluating interactive world models using 22 automated metrics validated against human judgments.
DSSP is a history-conditioned diffusion state space policy that uses SSMs to encode full observation streams with an auxiliary dynamics objective and hierarchical fusion, achieving SOTA results with reduced model size in robot manipulation.
A kinematic-to-visual lifting paradigm combined with hierarchically routed control generates action-conditioned surgical videos with better faithfulness, fidelity, and efficiency.
A video foundation model trained on human demonstrations generates zero-shot plans that convert to executable robot actions on novel scenes and tasks.
RoboWorld introduces an automated pipeline using autoregressive video world models and task-progress VLM scoring, plus Step Forcing for long-horizon stability, to achieve high correlation with real robot policy evaluation.
SC3-Eval enforces three consistencies on a video model to produce policy rollouts that correlate 0.929 with real-world performance across seven vision-language-action policies and reproduce observed failure modes.
A co-evolutionary VLM-VGM loop on 500 unlabeled images raises planner success by 30 points and simulator success by 48 percent while beating fully supervised baselines.
RISE combines a controllable dynamics model and progress value model into a closed-loop self-improving pipeline that updates robot policies entirely in imagination, reporting over 35% absolute gains on three real-world tasks.
CoLA-World jointly trains latent action models and world models with a warm-up phase to achieve co-evolution, matching or exceeding prior two-stage methods in video simulation quality and visual planning performance.
A controllable world model trained on the DROID dataset generates consistent multi-view robot trajectories for over 20 seconds and improves generalist policy success rates by 44.7% via imagined trajectory fine-tuning.
MV-WAM reports 55.7% simulation and 77.5% real-world success rates by aligning heterogeneous visual and action manifolds through causal masking and value-guided rollback.
WorldArena 2.0 extends embodied world model benchmarks to visuotactile perception, interactive policy training, and diverse real and simulated robotic platforms under a unified protocol.
Compositional Simulation generates scalable real-world robot training data by combining classical simulation with neural simulation in a closed-loop real-sim-real augmentation pipeline.
GE-Sim 2.0 is a video-based closed-loop simulator for robotic manipulation that adds state expert, world judge, and acceleration modules on top of prior video generation to support policy learning and evaluation.
An agentic framework generates executable physics simulation code from text prompts via coordinated planning, coding, visual, and physics agents that iterate to satisfy both prompt fidelity and physical constraints.
Cosmos-Predict2.5 unifies text-to-world, image-to-world, and video-to-world generation in one model trained on 200M clips with RL post-training, delivering improved quality and control for physical AI.
A survey that clarifies boundaries and organizes World Action Models by generation requirements and predictive substrates, identifying a trend toward generating less of the future.
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.
A survey of VLA robotics research identifies data infrastructure as the primary bottleneck and distills four open challenges in representation alignment, multimodal supervision, reasoning assessment, and scalable data generation.
The Cosmos platform supplies open-source pre-trained world models and supporting tools for building fine-tunable digital world simulations to train Physical AI.
citing papers explorer
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MiraBench: Evaluating Action-Conditioned Reliability in Robotic World Models
MiraBench defines action-conditioned reliability via three levels (physics adherence, action-following fidelity, optimism bias detection) and applies it to 12 model configurations using a 16,000-judgment human corpus, finding visual fidelity a poor proxy for action fidelity, no reliable scale benefi
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Coding Agent Is Good As World Simulator
An agentic framework generates executable physics simulation code from text prompts via coordinated planning, coding, visual, and physics agents that iterate to satisfy both prompt fidelity and physical constraints.