Proposes a self-evolving cognitive framework integrating causal world modeling, intervention-driven reasoning, and continual refinement for embodied scientific intelligence.
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A delay-aware RL approach learns transferable structured representations and dynamics via implicit causal graphs, outperforming baselines on delayed DMC tasks and accelerating adaptation to new tasks.
citing papers explorer
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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.
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Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
A delay-aware RL approach learns transferable structured representations and dynamics via implicit causal graphs, outperforming baselines on delayed DMC tasks and accelerating adaptation to new tasks.