VHYDRO is a support-safe variational hybrid filter that jointly recovers continuous latent states, discrete contact modes, and sparse port-Hamiltonian laws per regime while preventing loss of feasible transitions.
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In configurable enterprise systems, runtime discovery of transition dynamics from system configuration is more robust to deployment shifts than offline-trained world models.
WorldComp2D explicitly structures latent space geometry by object identity and spatial proximity via a proximity-dependent encoder and localizer, cutting parameters up to 4X and FLOPs 2.2X versus state-of-the-art lightweight models on facial landmark localization while staying real-time on CPU.
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
RAP turns LLMs into dual world-model and planning agents via MCTS to generate better reasoning paths, outperforming CoT baselines and achieving 33% relative gains over GPT-4 CoT using LLaMA-33B on plan generation.
Neural Control introduces adjoint-based differentiation through implicit equilibrium constraints to enable memory-efficient gradient computation and robust receding-horizon MPC for multi-stable deformable object manipulation, outperforming gradient-free baselines in simulation and hardware.
citing papers explorer
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Support-Safe Variational Hybrid Filtering for Contact-Mode and Sparse-Law Recovery
VHYDRO is a support-safe variational hybrid filter that jointly recovers continuous latent states, discrete contact modes, and sparse port-Hamiltonian laws per regime while preventing loss of feasible transitions.
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Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics
In configurable enterprise systems, runtime discovery of transition dynamics from system configuration is more robust to deployment shifts than offline-trained world models.
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WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views
WorldComp2D explicitly structures latent space geometry by object identity and spatial proximity via a proximity-dependent encoder and localizer, cutting parameters up to 4X and FLOPs 2.2X versus state-of-the-art lightweight models on facial landmark localization while staying real-time on CPU.
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Geometric Pareto Control: Riemannian Gradient Flow of Energy Function via Lie Group Homotopy
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
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Reasoning with Language Model is Planning with World Model
RAP turns LLMs into dual world-model and planning agents via MCTS to generate better reasoning paths, outperforming CoT baselines and achieving 33% relative gains over GPT-4 CoT using LLaMA-33B on plan generation.
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Neural Control: Adjoint Learning Through Equilibrium Constraints
Neural Control introduces adjoint-based differentiation through implicit equilibrium constraints to enable memory-efficient gradient computation and robust receding-horizon MPC for multi-stable deformable object manipulation, outperforming gradient-free baselines in simulation and hardware.