RopeDreamer uses quaternionic kinematic chains in a recurrent state space model with a dual decoder to cut open-loop prediction error by 40.52% over 50 steps on simulated DLO trajectories while preserving physical constraints.
Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification
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GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
SpectraMB performs target-oriented representation purification via dynamic spectral filtering and reliability-aware fusion via global-context attention to address intra-behavior entanglement and inter-behavior heterogeneity in multi-behavior recommendation.
SUPRA resolves the aggregation dilemma in MAGL by decoupling modality-specific MLP paths from a shared lightweight GNN, achieving SOTA performance with 3.5x lower memory and 4.4x faster training.
Introduces Hodge Spectral Duality, a hybrid neural architecture that applies Hodge orthogonality and operator splitting to isolate unlearnable topological degrees of freedom from learnable geometric dynamics in solution operators on geometric meshes.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects
RopeDreamer uses quaternionic kinematic chains in a recurrent state space model with a dual decoder to cut open-loop prediction error by 40.52% over 50 steps on simulated DLO trajectories while preserving physical constraints.
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Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
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Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation
SpectraMB performs target-oriented representation purification via dynamic spectral filtering and reliability-aware fusion via global-context attention to address intra-behavior entanglement and inter-behavior heterogeneity in multi-behavior recommendation.
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Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs
SUPRA resolves the aggregation dilemma in MAGL by decoupling modality-specific MLP paths from a shared lightweight GNN, achieving SOTA performance with 3.5x lower memory and 4.4x faster training.
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Topology-Preserving Neural Operator Learning via Hodge Decomposition
Introduces Hodge Spectral Duality, a hybrid neural architecture that applies Hodge orthogonality and operator splitting to isolate unlearnable topological degrees of freedom from learnable geometric dynamics in solution operators on geometric meshes.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.