Clustering Unstructured Text with LLM Embeddings and HDBSCAN
The current era of Generative AI seems to primarily focus on chat interfaces and prompts, but the range of applications of large language models , or LLMs for short, is not limited to just that.
5 Essential Approaches to Robust Outlier Detection
Outliers can easily ruin the performance of any predictive analysis models you build: robustly detecting and handling them is crucial in any data project. This article lists and compares five essential approaches for detecting them.
Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode
From installing Ollama to launching OpenCode with a local model, step by step.
The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science.
Over the past year I’ve reviewed enterprise agent architectures at roughly two dozen organizations, including banks, retailers, healthcare systems, and a couple of regulators. The architecture diagrams have been reliably impressive. There are boxes for the MCP gateway, the tool registry, the vector ...
The $400 million machine powering the future of chipmaking
Jos Benschop is climbing a ladder to get to the top of his newest machine. It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the groun...
Prime Intellect Releases prime-rl 0.6.0 to Train Trillion-Parameter MoE Models on Agentic RL Workloads
Prime Intellect has released prime-rl 0.6.0, an open framework for asynchronous reinforcement learning on trillion-parameter Mixture-of-Experts models. It trained GLM-5 on SWE tasks at up to 131k sequence length, with sub-5-minute step times and 256 rollouts, on 28 H200 nodes. This breakdown covers ...
NVIDIA Brings Trusted, 24/7 AI Agents to Telecom Operations
Telecom operators have seen remarkable returns from using generative AI to automate network management, customer care and back-office operations. Most of that impact has been task‑based: automation that speeds up predetermined steps while people manually correlate insights and direct next steps. Aut...
Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies
arXiv:2606.20599v1 Announce Type: new
Abstract: Tree of Thought (ToT) search has become a promising direction for improving the reasoning capabilities of large language models, but deploying these methods in practice raises a question that has received little systematic attention: how do different ...
The New Associationism: Lessons from Deep Learning
arXiv:2606.20600v1 Announce Type: new
Abstract: What can the success of modern AI tell us about how humans learn? This paper argues that taking AI seriously as a model of human learning supports a modest but genuine associationism. The central finding is that supervised learning -- learning driven ...
Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries
arXiv:2606.20615v1 Announce Type: new
Abstract: AI agents now participate as first-class team members across the software development lifecycle, yet no specification language exists for expressing the human-agent responsibility boundaries, approval gates, and governance constraints this collaborati...
arXiv:2606.20621v1 Announce Type: new
Abstract: Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduce persistent positional biases, amplify unreliable agents, and cause high sensitivity to role assignm...
Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix
By Zhuoning Yuan, Ta-Ying Cheng, Benjamin Klein, Bahareh AzarnoushIntroductionAt Netflix, we build technology to help storytellers bring their creative visions to life and to help members discover the stories they love.To connect stories with diverse audiences around the world, we produce promotiona...
Metric-Dependent Annotation Saturation for Learning from Label Distributions
When annotators disagree on a label, the disagreement itself carries signal—and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distributions subsampled from ChaosNLI, a dataset providing 100 independent annotator judgments per item, a...
Nine Judges, Two Effective Votes: Correlated Errors Undermine LLM Evaluation Panels
LLM-as-a-judge panels aggregate votes from multiple models, with the expectation that diverse models yield more reliable evaluations. We develop a framework to measure the true informational value of such panels and quantify how far their reliability falls short of the independent-voting ideal. Test...
By Alvin Bao, Alex Petrov, Jennifer Lai, Aidan Sherr, and Samartha ChandrashekarAs a part of the journey to transition Netflix’s compute infrastructure to be more Kubernetes-native, we have leaned into incorporating components from the Kubernetes ecosystem into our container platform Titus. One exam...
xAI Launches /goal in Grok Build, Adding Long-Running Autonomous Execution With Built-In Verification for Multi-Step Coding Tasks
xAI introduced /goal in Grok Build, a mode for long-running, autonomous task execution. You hand off one objective, and the agent plans an approach, executes a progress checklist, and verifies the result until the goal completes.
The post xAI Launches /goal in Grok Build, Adding Long-Running Autonom...