SiPeR improves recommendation accuracy and response quality in situated conversations by estimating scene transitions and performing Bayesian inverse inference with multimodal LLMs.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
RL agents in dynamic flows learn an adaptive flow-assisted casting strategy for odor search, showing non-monotonic performance with memory length explained by a sector-search model.
Higher-resolution observations with global-average-pooling encoders improve RL performance and generalization by enabling more localized visual attention, yielding up to 28% gains over standard Impala encoders.
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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Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation
SiPeR improves recommendation accuracy and response quality in situated conversations by estimating scene transitions and performing Bayesian inverse inference with multimodal LLMs.
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Emergence of a Flow-Assisted Casting Strategy for Olfactory Navigation via Memory-Augmented Reinforcement Learning
RL agents in dynamic flows learn an adaptive flow-assisted casting strategy for odor search, showing non-monotonic performance with memory length explained by a sector-search model.
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Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning
Higher-resolution observations with global-average-pooling encoders improve RL performance and generalization by enabling more localized visual attention, yielding up to 28% gains over standard Impala encoders.
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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.