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Amazon and University of Michigan give robots a sense of touch Capturing token IDs during agentic interactions for better reinforcement learning How Amazon tracks carbon intensity across its operations The fuel of the future is already here: Why TRISO matters AWS Graviton5: How a new chiplet architecture delivers 25% better performance - Amazon Science How formal verification makes AWS Nitro the first formally verified cloud hypervisor - Amazon Science Four approaches to grounding AI agents in the physical world - Amazon Science Bridging intent and execution in agentic systems - Amazon Science Ground truth is a process, not a dataset - Amazon Science How flat is replacing fat in AWS data center networks - Amazon Science Amazon Research Awards recipients announced - Amazon Science Training LLMs to reason in oarallel: How global forking tokens improve accuracy - Amazon Science New scaling law connects LLM architecture to inference efficiency, boosting throughput up to 47% - Amazon Science Promptimus: Improving already good LLM prompts with zero manual engineering - Amazon Science How Amazon optimizes middle-mile delivery networks under uncertainty - Amazon Science How mechanism design theory helps optimize Amazon-vendor collaboration - Amazon Science Inside Amazon's responsible-AI pipeline - Amazon Science How to train AI on private data without exposing it - Amazon Science How catastrophic is your LLM? A statistical framework for certifying conversational risk - Amazon Science Isabelle/HOL: The proof assistant behind the Nitro Isolation Engine - Amazon Science Customized Amazon Nova models improve molecular-property prediction in drug discovery - Amazon Science AWS and Hopkins Engineering announce groundbreaking database for AI/ML antibody design - Amazon Science How Amazon uses agentic AI for vulnerability detection at global scale - Amazon Science Verifying and optimizing post-quantum cryptography at Amazon - Amazon Science Improving quality and robustness in LLM-based text-to-speech systems - Amazon Science Formally verified AES-XTS: The first AES algorithm to join s2n-bignum - Amazon Science Optimizing LoRA target module selection for efficient fine tuning - Amazon Science How agentic AI helps heal the systems we can’t replace - Amazon Science Designing user experience for agentic AI: A framework for human-AI coordination - Amazon Science Intelligence isn’t about parameter count. It’s about time. - Amazon Science Why a 12-year-old forecasting paper has stood the test of time - Amazon Science How academic collaboration delivers real-world security to Amazon customers - Amazon Science Amazon Nova AI Challenge returns with Nova Forge access for competing teams - Amazon Science
How AI is changing the nature of mathematical research - Amazon Science
Michael Kearns, Aaron Roth · 2026-03-10 · via Amazon Science homepage
What machine learning theorists learned using AI agents to generate proofs — and what comes next.

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