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Diffusion models linked to associative memory dynamics

The article examines diffusion models through the lens of Dense Associative Memories. It describes how these models create distinct attractors for each training sample in the small data regime. As the training set size increases, the models transition to generalization. A critical intermediate phase is identified where spurious states emerge, marking the first signs of…

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Building reliable AI agents for production environments

A new technical guide details how to construct advanced AI agents that maintain stability in production settings. The article covers the ReAct architecture, planning loops, and state management using event sourcing. It also explains durable execution with Azure Durable Functions and multi-agent collaboration patterns. The author emphasizes placing reliability in deterministic code rather than relying…

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PLA and Microsoft launch free AI training for public libraries

The Public Library Association and Microsoft announced a partnership to provide no-cost artificial intelligence skills training for library staff and patrons. The initiative, titled Building AI Skills and Agency Through Libraries, aims to help communities use AI responsibly while protecting privacy and evaluating accuracy. A free webinar series and virtual workshops will begin in December…

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Researchers map the architecture of autonomous AI agents

A new review in Artificial Intelligence Review synthesizes 107 studies to define a unified five-module architecture for autonomous agents. The framework includes profile, memory, planning, action, and security components to standardize how these systems are described. The authors found that integrating domain knowledge and using role specialization significantly improves agent performance across various applications. However,…

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MIT researchers develop self-assembling neural networks

A team from MIT demonstrated that neural networks can form useful learning circuits from random connections using local plasticity rules. The study, published in the Proceedings of the National Academy of Sciences, introduces a framework called Self-Assembling Learning. The resulting networks achieved performance similar to those trained with backpropagation on image classification tasks. This approach…

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