technocracy

Advancing AI requires more than breakthrough models. It depends on communities of builders and researchers who experiment, test assumptions, and share what they learn. That belief is guiding how Amazon engages developers and academics around Amazon Nova, Amazon’s portfolio of AI offerings including the Nova models, Nova Forge and Nova Act. Today, two Nova initiatives launch in parallel, each designed for a distinct audience but connected by a common purpose: to help people innovate, build skills, and tackle […]

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arXiv:2602.09263v1 Announce Type: new Abstract: Cloud-mediated IoT architectures fragment authentication across vendor silos and create latency and availability bottlenecks for cross-vendor device-to-device (D2D) interactions. We present Atlas, a framework that extends the Web public-key infrastructure to IoT by issuing X.509 certificates to devices via vendor-operated ACME clients and vendor-controlled DNS namespaces. Devices obtain globally verifiable identities without hardware changes and establish mutual TLS channels directly across administrative domains, decoupling runtime authentication from cloud reachability. We prototype Atlas on […]

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arXiv:2601.03276v1 Announce Type: new Abstract: Topic segmentation using generative Large Language Models (LLMs) remains relatively unexplored. Previous methods use semantic similarity between sentences, but such models lack the long range dependencies and vast knowledge found in LLMs. In this work, we propose an overlapping and recursive prompting strategy using sentence enumeration. We also support the adoption of the boundary similarity evaluation metric. Results show that LLMs can be more effective segmenters than existing methods, but issues remain to […]

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arXiv:2602.07145v1 Announce Type: new Abstract: Deep learning has non-convex loss landscape and its optimization dynamics is hard to analyze or control. Nevertheless, the dynamics can be empirically convex-like across various tasks, models, optimizers, hyperparameters, etc. In this work, we examine the applicability of convexity and Lipschitz continuity in deep learning, in order to precisely control the loss dynamics via the learning rate schedules. We illustrate that deep learning quickly becomes weakly convex after a short period of training, […]

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The A.I. Disruption We’ve Been Waiting for Has Arrived New opinion piece from Paul Ford in the New York Times. Unsurprisingly for a piece by Paul it’s packed with quoteworthy snippets, but a few stood out for me in particular. Paul describes the November moment that so many other programmers have observed, and highlights Claude Code’s ability to revive old side projects: [Claude Code] was always a helpful coding assistant, but in November it suddenly got much better, […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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AI companies teaming up with some of the big retail stores is becoming a new trend. For those who still haven’t got why, the reason is pretty obvious: making users shop more smartly. However, the news of Google teaming up with Walmart isn’t just another partnership at all. Both companies are looking to revolutionize how people shop, and this time, it’s not just another AI experiment slapped onto an app. Soon, Gemini will make Walmart shopping easier than […]

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arXiv:2604.13068v1 Announce Type: new Abstract: When do large language models decide to hallucinate? Despite serious consequences in healthcare, law, and finance, few formal answers exist. Recent work shows autoregressive models maintain internal representations distinguishing factual from fictional outputs, but when these representations peak as a function of model scale remains poorly understood. We study the temporal dynamics of hallucination-indicative internal representations across 7 autoregressive transformers (117M–7B parameters) using three fact-based datasets (TriviaQA, Simple Facts, Biography; 552 labeled examples). […]

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arXiv:2602.20216v1 Announce Type: new Abstract: Robot-assisted endovascular intervention offers a safe and effective solution for remote catheter manipulation, reducing radiation exposure while enabling precise navigation. Reinforcement learning (RL) has recently emerged as a promising approach for autonomous catheter steering; however, conventional methods suffer from sparse reward design and reliance on static vascular models, limiting their sample efficiency and generalization to intraoperative variations. To overcome these challenges, this paper introduces a sample-efficient RL framework with online expert correction for […]

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Model-based offline reinforcement learning (RL) aims to enhance offline RL with a dynamics model that facilitates policy exploration. However, textit{model exploitation} could occur due to inevitable model errors, degrading algorithm performance. Adversarial model learning offers a theoretical framework to mitigate model exploitation by solving a maximin formulation. Within such a paradigm, RAMBO~citep{rigter2022rambo} has emerged as a representative and most popular method that provides a practical implementation with model gradient. However, we empirically reveal that severe Q-value underestimation and […]

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