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1,990 papers
Quantum Electronics Explained: Quantum Systems, Devices & Advanced Technologies is a publication-grade Open Educational Resource (OER) module covering the physical principles, macroscopic quantum mechanics, circuit quantum electrodynamics (cQED), and cryogenic microwave control governing quantum hardware. Serving as an...
The integration of symbolic reasoning with deep reinforcement learning presents a promising paradigm for achieving transparent and interpretable sequential decision-making in complex environments. This work introduces NeuroSymbolic-RLNet, a novel hybrid framework that combines symbolic state transition graphs with neur...
This review-style study examines how reinforcement learning agents and graph neural networks are being integrated into gate-level logic synthesis for electronic design automation. It discusses how GNN-derived structural embeddings support pre-layout estimation of signal probability and switching activity, how RL agents...
Mobile Edge Computing (MEC) enables resource-constrained mobile devices to offload computation-intensive tasks to nearby edge servers. Existing computation offloading approaches primarily optimise latency, energy consumption, or resource allocation, but often do not consider security constraints and multi-user queue st...
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ABSTRACT In view of the development of semiconductor manufacturing technology to the sub-3nm nodes, traditional Electronic Design Automation (EDA) approaches face challenges when navigating through the huge search space associated with multi-objective optimization in the realm of Power, Performance, and Area (PPA). Cla...
Large language models encode behavioral constraints directly within their latent representations. Existing approaches to modifying these behaviors rely on parameter-level adjustments (Supervised Fine-Tuning, Reinforcement Learning from Human Feedback, or permanent weight abliteration). These irreversibly alter model we...
Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.
Electric vehicles (EVs) have experienced vigorous development in recent years. However, their large‐scale integration into the power grid presents challenges related to the ‘curse of dimensionality’ and uncertainties, making it difficult to balance rapid grid demand response with the interests of EV users. To overcome...
ABSTRACT In view of the development of semiconductor manufacturing technology to the sub-3nm nodes, traditional Electronic Design Automation (EDA) approaches face challenges when navigating through the huge search space associated with multi-objective optimization in the realm of Power, Performance, and Area (PPA). Cla...
In 1937, Jorge Luis Borges looked back some 650 years at Ramon Llull‘s machinic ars inveniendi. This is one of five essays that looks at contemporary learning machines through the lens of Borges‘s characteristically engimatic historical note and literary reflection. It examines the epistemological tension between deter...
EL-RAKHAWI DOCTRINE OF TOPOLOGICAL MORAL GRAVITY: EXECUTIVE SUMMARY This treatise establishes Topological Moral Gravity, reframing AI ethics from external rules to the fundamental geometry of the decision space. We prove standard gradient descent operates in a morally flat Euclidean space. Our solution: bending the dec...
EL-RAKHAWI DOCTRINE OF TOPOLOGICAL MORAL GRAVITY: EXECUTIVE SUMMARY This treatise establishes Topological Moral Gravity, reframing AI ethics from external rules to the fundamental geometry of the decision space. We prove standard gradient descent operates in a morally flat Euclidean space. Our solution: bending the dec...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Mobile Systems
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