ω-EVA is a three-stage latent world model framework that trains action-conditioned dynamics, a language-conditioned flow policy, and a tri-branch refiner to improve embodied action generation in simulation.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
Proposes a self-evolving cognitive framework integrating causal world modeling, intervention-driven reasoning, and continual refinement for embodied scientific intelligence.
A systematic review of text world models as transition predictors for LLM agents, organized by foundations, construction methods, applications in planning/training, and evaluation.
The paper describes an open-source web application that applies generative world models to produce interactive cinematic explorations of quantum computing hardware grounded in AWS Braket specifications.
A tutorial that unifies explicit and implicit world models through shared predictive structure for applications in physical AI such as robotics.
citing papers explorer
-
$\omega$-EVA: Envision, Verify, and Act with Latent Interactive World Models
ω-EVA is a three-stage latent world model framework that trains action-conditioned dynamics, a language-conditioned flow policy, and a tri-branch refiner to improve embodied action generation in simulation.
-
Self-Evolving Cognitive Framework via Causal World Modeling for Embodied Scientific Intelligence
Proposes a self-evolving cognitive framework integrating causal world modeling, intervention-driven reasoning, and continual refinement for embodied scientific intelligence.
-
Bridging the Agent-World Gap: Text World Models for LLM-based Agents
A systematic review of text world models as transition predictors for LLM agents, organized by foundations, construction methods, applications in planning/training, and evaluation.
-
Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Models
The paper describes an open-source web application that applies generative world models to produce interactive cinematic explorations of quantum computing hardware grounded in AWS Braket specifications.
-
A Tutorial on World Models and Physical AI
A tutorial that unifies explicit and implicit world models through shared predictive structure for applications in physical AI such as robotics.