LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
Attention is all you need
8 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Adaptive Clustering router for MoE models that scales features to identify tight expert clusters, yielding faster convergence, robustness to corruption, and performance gains.
ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.
MORL generates more diverse requirement-violation scenarios while SORL produces higher-severity violations when testing interdependent requirements in an end-to-end AV controller.
Systematic tests of VLM backbones, policy architectures, and cross-embodiment data yield RoboVLMs that set new SOTA on robot manipulation benchmarks while requiring few manual designs.
IMPACTX adds XAI-derived attention maps as a training constraint on standard CNNs, raising accuracy on CIFAR-10/100 and STL-10 while embedding feature attributions directly in the model.
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
Hunyuan3D 2.0 scales flow-based diffusion transformers and texture synthesis models to generate high-resolution textured 3D assets that outperform prior state-of-the-art in geometry, alignment, and texture quality.
citing papers explorer
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LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
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Tight Clusters Make Specialized Experts
Introduces Adaptive Clustering router for MoE models that scales features to identify tight expert clusters, yielding faster convergence, robustness to corruption, and performance gains.
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Deep Learning Alternatives of the Kolmogorov Superposition Theorem
ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.
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Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study
MORL generates more diverse requirement-violation scenarios while SORL produces higher-severity violations when testing interdependent requirements in an end-to-end AV controller.
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What Matters in Building Vision-Language-Action Models for Generalist Robots
Systematic tests of VLM backbones, policy architectures, and cross-embodiment data yield RoboVLMs that set new SOTA on robot manipulation benchmarks while requiring few manual designs.
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IMPACTX: improving model performance by appropriately constraining the training with teacher explanations
IMPACTX adds XAI-derived attention maps as a training constraint on standard CNNs, raising accuracy on CIFAR-10/100 and STL-10 while embedding feature attributions directly in the model.
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Large Language Models for Multi-Robot Systems: A Survey
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
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Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
Hunyuan3D 2.0 scales flow-based diffusion transformers and texture synthesis models to generate high-resolution textured 3D assets that outperform prior state-of-the-art in geometry, alignment, and texture quality.