When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
arXiv:2606.17220v1 Announce Type: new
Abstract: Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that...
SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions
arXiv:2606.17266v1 Announce Type: new
Abstract: Production planning increasingly has to treat workforce capability as a decision variable: certifications lapse when skills are not maintained, new products require skills the current workforce does not hold, and reskilling competes for the same worke...
Skill-Constrained Model Predictive Control for Resilient Manufacturing Supply Chains
arXiv:2606.17269v1 Announce Type: new
Abstract: In skill-constrained production-inventory systems, the qualified human capacity available tomorrow depends on training decisions made today: production requires certified workers, certifications decay unless maintained, and training consumes the same ...
Nothing from Something: Can a Language Model Discover 0?
arXiv:2606.17289v1 Announce Type: new
Abstract: AI systems based on artificial neural networks are being developed with aspirations of pushing the boundary of human mathematical knowledge. A key question for these systems is how much they can reach beyond their training data. Mathematical discovery...
AI runs at the speed of light. More and more, that light is made in Texas. Coherent broke ground today on an expanded manufacturing building in Sherman, Texas. The company makes the lasers, optical components and compound semiconductors that wire AI systems together — and runs what it calls the wor...
MIT’s Initiative for New Manufacturing builds momentum
In its first year, INM has worked across research, workforce development, and industry engagement to help accelerate new manufacturing technologies and their real-world deployment.
Android 17 launches with new multitasking tools as Google expands Gemini features
Google has released Android 17 and Wear OS 7, introducing new multitasking features, parental controls, security tools, and smartwatch upgrades. The launch is also accompanied by a Pixel Drop that brings Google’s latest AI models to its devices.
Meet Qwen-RobotSuite: Three Embodied AI Models for VLA Manipulation, Video World Modeling, and Navigation
We break down Qwen-RobotSuite, the Qwen team's three new embodied AI models. We cover RobotManip, a Vision-Language-Action model built on Qwen3.5-4B for manipulation. We cover RobotWorld, a language-conditioned video world model with a 60-layer MMDiT. We cover RobotNav, a navigation model built on Q...
HPE AI Factory With NVIDIA Expands for the Era of Agents
Enterprises are moving agentic AI from proof of concept to production — and the next generation of AI factories are built for the era of agents. At HPE Discover Las Vegas, running through Thursday, June 18, NVIDIA and HPE are expanding the HPE AI Factory with NVIDIA, including NVIDIA Vera CPU and NV...
Fastest, Largest, Strongest: NVIDIA Blackwell Sweeps MLPerf Training 6.0
Every breakthrough AI model starts the same way: with a training run. The infrastructure running those training jobs shapes everything: how fast teams can iterate, what scale of model they can build and whether those jobs complete reliably. As models grow in size, complexity and intelligence, the d...
Tired of your monthly API bill? Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches.
The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science.
LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity durin...
Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vecto...
Many AI agent systems become economically unsustainable long before they become technically impressive. Teams usually focus on model choice, prompt design, tool calling, and orchestration. Those things matter, but they are only part of the system setup. The deeper issue is that coding agents, such a...