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<title>AI News: Amazon Science</title>
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<description>Latest publications from Amazon Science, aggregated by AI News from the official source.</description>
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<lastBuildDate>Fri, 23 Oct 2026 19:08:30 GMT</lastBuildDate>
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<item><title>Amazon Science: Better speech synthesis for audiobooks</title><link>https://www.amazon.science/blog/better-speech-synthesis-for-audiobooks</link><guid isPermaLink="false">https://www.amazon.science/blog/better-speech-synthesis-for-audiobooks</guid><pubDate>Fri, 23 Oct 2026 15:20:02 GMT</pubDate><category>research</category><description>“Controllable text to speech” makes speech more expressive, character voices more distinct and consistent.</description></item>
<item><title>Amazon Science: The science behind TorchNeuron, Amazon’s new PyTorch backend for AWS Trainium chips</title><link>https://www.amazon.science/blog/the-science-behind-torchneuron-amazons-new-pytorch-backend-for-aws-trainium-chips</link><guid isPermaLink="false">https://www.amazon.science/blog/the-science-behind-torchneuron-amazons-new-pytorch-backend-for-aws-trainium-chips</guid><pubDate>Thu, 22 Oct 2026 19:36:42 GMT</pubDate><category>research</category><description>Runtime optimizations and a thorough evaluation process driven by agentic AI help ensure that models written in PyTorch will execute efficiently and reliably on Trainium systems.</description></item>
<item><title>Amazon Science: Strengthening AI safety evaluation through independent third-party review</title><link>https://www.amazon.science/blog/strengthening-ai-safety-evaluation-through-independent-third-party-review</link><guid isPermaLink="false">https://www.amazon.science/blog/strengthening-ai-safety-evaluation-through-independent-third-party-review</guid><pubDate>Tue, 20 Oct 2026 17:57:55 GMT</pubDate><category>research</category><description>How Amazon partnered with AVERI to independently assess — and improve — our internal safety testing of Amazon models.</description></item>
<item><title>Amazon Science: Can we change an AI model’s architecture without retraining it from scratch?</title><link>https://www.amazon.science/blog/can-we-change-an-ai-models-architecture-without-retraining-it-from-scratch</link><guid isPermaLink="false">https://www.amazon.science/blog/can-we-change-an-ai-models-architecture-without-retraining-it-from-scratch</guid><pubDate>Mon, 12 Oct 2026 17:36:18 GMT</pubDate><category>research</category><description>“Priming” reuses pretrained Transformer weights to build models with more efficient architectures at less than 1% of the original training cost and with throughputs up to twice as high.</description></item>
<item><title>Amazon Science: Graph-centric agentic intelligence</title><link>https://www.amazon.science/blog/graph-centric-agentic-intelligence</link><guid isPermaLink="false">https://www.amazon.science/blog/graph-centric-agentic-intelligence</guid><pubDate>Thu, 01 Oct 2026 12:54:31 GMT</pubDate><category>research</category><description>Augmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures.</description></item>
<item><title>Amazon Science: A kernel-centric path to real-time video generation on Trainium</title><link>https://www.amazon.science/blog/a-kernel-centric-path-to-real-time-video-generation-on-trainium</link><guid isPermaLink="false">https://www.amazon.science/blog/a-kernel-centric-path-to-real-time-video-generation-on-trainium</guid><pubDate>Fri, 25 Sep 2026 14:55:00 GMT</pubDate><category>research</category><description>Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.</description></item>
<item><title>Amazon Science: Amazon launches research initiative with Stanford University to advance AI and science</title><link>https://www.amazon.science/news/amazon-launches-research-initiative-with-stanford-university-to-advance-ai-and-science</link><guid isPermaLink="false">https://www.amazon.science/news/amazon-launches-research-initiative-with-stanford-university-to-advance-ai-and-science</guid><pubDate>Mon, 21 Sep 2026 19:31:59 GMT</pubDate><category>research</category><description>The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.</description></item>
<item><title>Amazon Science: Advancing AI for biology: Teaching models to design and characterize antibodies</title><link>https://www.amazon.science/blog/advancing-ai-for-biology-teaching-models-to-design-and-characterize-antibodies</link><guid isPermaLink="false">https://www.amazon.science/blog/advancing-ai-for-biology-teaching-models-to-design-and-characterize-antibodies</guid><pubDate>Mon, 21 Sep 2026 17:51:00 GMT</pubDate><category>research</category><description>Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.</description></item>
<item><title>Amazon Science: Why don’t machine learning research agents overfit?</title><link>https://www.amazon.science/blog/why-dont-machine-learning-research-agents-overfit</link><guid isPermaLink="false">https://www.amazon.science/blog/why-dont-machine-learning-research-agents-overfit</guid><pubDate>Thu, 10 Sep 2026 15:03:39 GMT</pubDate><category>research</category><description>New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.</description></item>
<item><title>Amazon Science: Developing provably correct Rust code with Verus</title><link>https://www.amazon.science/blog/developing-provably-correct-rust-code-with-verus</link><guid isPermaLink="false">https://www.amazon.science/blog/developing-provably-correct-rust-code-with-verus</guid><pubDate>Mon, 31 Aug 2026 15:35:33 GMT</pubDate><category>research</category><description>How the Verus &quot;program verifier&quot;, which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.</description></item>
<item><title>Amazon Science: When LLM judges agree, should we believe them?</title><link>https://www.amazon.science/blog/when-llm-judges-agree-should-we-believe-them</link><guid isPermaLink="false">https://www.amazon.science/blog/when-llm-judges-agree-should-we-believe-them</guid><pubDate>Wed, 26 Aug 2026 17:10:40 GMT</pubDate><category>research</category><description>Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.</description></item>
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