As integrated circuit fabrication approaches physical limits, classical Electronic Design Automation (EDA) algorithms struggle with high-dimensional search spaces during multi-objective Power, Performance, and Area (PPA) optimization. Binary addition circuits form the core bottleneck of arithmetic processing units; thu...
Enrico Jose Gonzaga· Zenodo (CERN European Organi...· 0 citations
AbstractProgrammable Logic Controllers (PLCs) rely on fixed-threshold comparators that frequently trigger false emergency trips when exposed to electromagnetic interference, thermal drift, and mechanical vibrations. This paper evaluates applying machine learning to industrial logic gate synthesis, using Graph Neural Ne...
Nico Ivan Rosales· Zenodo (CERN European Organi...· 0 citations
Vision-based swimmer and drowning detection systems, built primarily on lightweight YOLO architectures, have achieved measurable gains in accuracy while shrinking model size for resource-constrained edge hardware. However, Field-Programmable Gate Array (FPGA) accelerators hosting these models continue to rely on conven...
Luigi Bautista· Zenodo (CERN European Organi...· 0 citations
AbstractProgrammable Logic Controllers (PLCs) rely on fixed-threshold comparators that frequently trigger false emergency trips when exposed to electromagnetic interference, thermal drift, and mechanical vibrations. This paper evaluates applying machine learning to industrial logic gate synthesis, using Graph Neural Ne...
Nico Ivan Rosales· Zenodo (CERN European Organi...· 0 citations
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As integrated circuit fabrication approaches physical limits, classical Electronic Design Automation (EDA) algorithms struggle with high-dimensional search spaces during multi-objective Power, Performance, and Area (PPA) optimization. Binary addition circuits form the core bottleneck of arithmetic processing units; thu...
Enrico Jose Gonzaga· Zenodo (CERN European Organi...· 0 citations
Preprint · Version 1 · September 2026 · Not peer reviewed If the limits of language are the limits of the thinkable, then the possibility of linguistic superintelligence depends on whether a system can expand its own language. This paper argues that large language models (LLMs) cannot do so in a closed regime. We intro...
Carlos Alberto Sierra Murillo· Zenodo (CERN European Organi...· 0 citations
Abstract of Cache-Fused Kinematic Rails: Ex-Ante Silicon Alignment via In-Vivo KV-Cache Metric Grafting Current Large Language Model (LLM) alignment relies primarily on post-hoc, output-level correction mechanisms such as external API meta-governors, Reinforcement Learning from Human Feedback (RLHF), or post-generation...
Daniel Solis· Zenodo (CERN European Organi...· 0 citations
Hardware-aware quantization frameworks such as HAQ use reinforcement learning (RL)to search per-layer bit-width policies, but this search itself requires hundreds to thousands ofpolicy evaluations with an accelerator (and, in HAQ’s case, per-episode fine-tuning) in theloop — a resource requirement that is at odds with...
Alaa eldeen Abdelrahman· Zenodo (CERN European Organi...· 0 citations
FINDING: The search results are video titles and abstracts, not actual mathematical solutions. No equations, constants, or derivations are present. The only concrete mathematical content is a reference to an arXiv paper on robotic garment folding (LeHome Challenge 2026), which is applied robotics, not pure competition...
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
rslcpp - the ROS simulation library for C++ - lets you turn a set of existing ROS 2 nodes into a deterministic simulation. Instead of the asynchronous, multi-process default execution of ROS 2, rslcpp aggregates all participating nodes in a single process and drives them through a custom executor under an explicit simu...
Simon Sagmeister, Marcel Weinmann· Zenodo (CERN European Organi...· 0 citations
Cell-Free (CF) massive Multiple-Input Multiple Output (MIMO) networks are a key enabler for future sixth generation wireless systems, as they allow distributed Radio Units (RUs) to cooperatively serve users and mitigate inter-user interference. However, under dense deployments and high spatial loading, conventional lin...
Mohammad Eskandarinia, John S. Vardakas, Christos V. Verikoukis· Zenodo (CERN European Organi...· 0 citations
This article provides a beginner-friendly introduction to Machine Learning and explains how artificial intelligence systems learn patterns from data. It discusses the role of data, the training process, and the main approaches to machine learning, including supervised learning, unsupervised learning, and reinforcement...
Sofia Boukefa· Zenodo (CERN European Organi...· 0 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
