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An arXiv paper describes how Netflix built, deployed and continuously monitored an LLM judge for recommendation explanations, reporting viewing and engagement gains in a five-week A/B test involving tens of millions of members.
A new arXiv survey proposes viewing an AI agent’s memories, tools, skills, workflows and relationships as a graph that changes over time, and calls for graph-aware evaluation and governance.
A new arXiv preprint presents FACET, a framework for generating executable terminal tasks whose instructions, environments, solutions and verifiers are designed to remain consistent.
An arXiv preprint introduces SESSE, a training-free framework that breaks an LLM judge’s preference into sub-questions. The authors report near-parity with a chain-of-thought baseline on 1,000 RewardBench examples and criterion-level vote records; generalization, cost, and independent validation remain open questions.
An IBM Spyre team says coding agents helped create 13 runtime adapters that covered 7,960 of the 10,000 most-downloaded Hugging Face embedding models in its target set, with 6,804 passing end-to-end tests on Spyre. The team says human debugging remained essential.
An Apple research paper describes a three-phase iterative pseudo-labeling method for Mandarin-English code-switching automatic speech recognition and reports Mix Error Rate reductions on two SEAME development subsets.
Google says users will soon be able to tell Discover what topics and links they want to see more or less of, while new controls also personalize Search and Google News audio briefings.
Amazon Bedrock now offers OpenAI’s GPT-5.6 Sol, Terra, and Luna models in more than 25 AWS Regions, with geographic and global routing options that expand the available compute pool.
Google says AI Overviews and AI Mode will now highlight users’ preferred sources, while new carousels and a Highly Cited badge aim to make original reporting and firsthand perspectives easier to find.
MIT researchers report a density-functional-theory and machine-learning approach for identifying catalyst alloys that could improve electrochemical ammonia production, though the proposed materials have not yet been made or tested.
Emerald AI, NVIDIA, EPRI, National Grid and Nebius tested software that reduced an AI factory’s electricity use during simulated grid emergencies while preserving selected high-priority workloads. One test reported roughly 30% less use within 40 seconds; commercial and grid-wide benefits remain unproven.
Ramp says Router gives developers one endpoint for multiple AI models and can lower inference costs through automated model selection, but key performance and retention details remain unverified.
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