ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World introduces a framework designed to automate the process of teaching robots complex physical s...
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ENPIRE: Agentic Robot Policy Self-Improvement in the Real World introduces a framework designed to automate the process of teaching robots complex physical s...
Modularity-Free Conflict-Averse Training for Generalized PINNs Physics-informed neural networks (PINNs) are a popular tool for solving complex partial differ...
FlowEdit: Associative Memory for Lifelong Pronunciation Adaptation in Flow-Matching TTS Modern text-to-speech (TTS) systems are highly capable but suffer fro...
Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufa...
A Multi-Agent system for Multi-Objective constrained optimization In many computing and networking systems, decision-making involves a difficult balancing ac...
MetaResearcher: Scaling Deep Research via Self-Reflective Reinforcement Learning in Adversarial Virtual Environments The MetaResearcher framework aims to evo...
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents AI agents in customer service often struggle to maintain a clear picture of a task as i...
Reinforcement learning (RL) has become a powerful tool for improving world models, but current methods often struggle with "reward hacking"—where a model lea...
Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning This research addresses the challenge of automating sperm morphology analysi...
eCNNTO: A Highly Generalizable ConvNet for Accelerating Topology Optimization Topology optimization is a powerful engineering tool used to determine the most...
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