Abstract: In the rapidly advancing Reinforcement Learning (RL) field, Multi-Agent Reinforcement Learning (MARL) has emerged as a key player in solving complex real-world challenges. A pivotal ...
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.
Meta Platform’s announcement on Monday that it has acquired Chinese agent startup Manus represents a big win for Manus’ ...
A practical guide to the four strategies of agentic adaptation, from "plug-and-play" components to full model retraining.
This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
Click the "Fork" button at the top of this page This creates your own copy at: github.com/YOUR_USERNAME/production-ready-data-science-code Clone your fork: ...
At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...
Abstract: This paper studies how AI-assisted programming and large language models (LLM) improve software developers' ability via AI tools (LLM agents) like Github Copilot and Amazon CodeWhisperer, ...
Inspired by the impressive reasoning capabilities demonstrated by reinforcement learning approaches like DeepSeek-R1, PeRL addresses a critical limitation in current multimodal reinforcement learning: ...
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