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...
Sakana AI Launches Sakana Fugu: An Orchestration Model That Routes Tasks Across a Swappable Pool of Frontier LLMs
Fugu and Fugu Ultra route tasks across a swappable model pool, leading most coding, reasoning, and agentic benchmarks.
The post Sakana AI Launches Sakana Fugu: An Orchestration Model That Routes Tasks Across a Swappable Pool of Frontier LLMs appeared first on MarkTechPost.
Three things to watch amid Anthropic’s latest feud with the government
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. For those of you enjoying your summer unaware of Anthropic’s latest feud with the US government, here’s a recap: In April the company said it had built an AI mode...
Agentic AI is not failing because the technology is bad. It is failing because of five specific misconceptions that teams carry into their first deployments and each one is correctable.
The founder conference built for growth: TechCrunch Founder Summit pass rates increase June 26
Save up to $190 on your pass to TechCrunch Founder Summit 2026 by June 26, 11:59 p.m. PT. Designed for founders first on November 4 in Boston. Register here.
NVIDIA Vera CPU Opens the Way for Agentic Scientific AI at Los Alamos National Laboratory
Mission, Vision and Veritas — new Los Alamos National Laboratory (LANL) supercomputers to be built with HPE and NVIDIA — are tapping NVIDIA Vera CPUs to accelerate scientific discovery, unlocking agentic AI for science. The supercomputers will use the HPE Cray Supercomputing GX5000 architecture with...
From Materials Simulation to Experimental Astronomy, New NVIDIA AI Software Unlocks Scientific Discoveries
At the ISC conference running in Hamburg this week, NVIDIA is introducing new software that speeds AI for science, from chemistry and materials discovery to the search for dark matter. The NVIDIA DAQIRI library and new NVIDIA ALCHEMI NIM microservices — as well as the NVIDIA cuPhoton reference code...
Eco Wave Power Turns Waves Into Watts With NVIDIA AI Infrastructure and Digital Twins
The next era of AI will not be defined by compute alone. Its growth will be determined by energy. As accelerated computing scales across AI factories, agentic AI, industrial AI, edge computing and physical AI — including robotics and autonomous systems — global electricity demand is rising at unpre...
3 NLTK Tricks for Advanced Text Preprocessing & Linguistic Analysis
In this article, we will walk through three essential NLTK tricks to elevate your text preprocessing: preserving phrase integrity with the MWETokenizer, context-aware lemmatization with POS mapping, and statistical collocation extraction using association measures.