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Apple Machine Learning studies how to mix scarce target-domain data with abundant generic data during language-model pretraining. Across more than 2,000 training runs, the research examines the trade-off between insufficient target exposure, repeated examples, diminishing returns, and overfitting.
Hudson River Trading signed a multiyear agreement with CoreWeave to use its AI computing services. The partnership will support the development of new trading research and machine-learning models.
A researcher applied the same SFT and GRPO recipe to three from-scratch LLMs ranging from 316M to 672M parameters, but observed sharply different outcomes. GRPO barely affected the smallest model, severely degraded the middle model, and caused modest degradation in the largest, with no GSM8K transfer despite curriculum learning.
A Reddit discussion thread tracks the expected release of EMNLP 2026 notifications and results. The post invites researchers to share updates while hoping to attend the conference in Budapest.
The article examines cosmological evolution, a fringe theory proposing that universes reproduce with variations and undergo a Darwinian form of natural selection. It also highlights how James Webb Space Telescope observations and machine learning may inspire new theories about the nature of the universe.
Amazon Bedrock AgentCore now supports Policy Authoring, which converts natural-language policy documents into Dogwood policies. The update helps teams enforce agent controls, including new time-based constraints, with worked examples and best practices.
AWS Professional Services uses a multi-agent framework on Amazon Bedrock AgentCore to automate enterprise cloud migrations end to end. Specialized agents cover discovery, infrastructure-as-code generation, portfolio governance, and post-migration operations, reducing IaC development time from weeks to minutes.
Amazon Bedrock AgentCore Web Search now supports per-request domain and publication-date filters. Developers can control which websites agents use and require fresher sources, with filtering enforced server-side.
AWS presents three serverless patterns for invoking Amazon Bedrock AgentCore agents asynchronously from AWS Step Functions. Task-token callbacks, direct service integrations, and durable functions help eliminate idle compute costs while agents process requests.
KnowledgeForge converts resolved ITSM incident tickets into new knowledge base articles and continuously curates existing content. Its multi-tenant, closed-loop pipeline uses Amazon Bedrock, Amazon S3 Vectors, and AWS Step Functions for deduplication, quality scoring, and content improvement.