arXiv Computer Vision
Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction
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ReWorld: Representation Learning for World Action Models
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Co-Evolving Acting and Forecasting for Language Agents
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Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning
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RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations
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PAVE: Predictive Alignment and Value-Guided Evolution for World-Action Policies
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arXiv Machine Learning
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Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
arXiv:2606. 09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback.