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Conferences Our experts present and discuss cutting-edge research at scientific meetings globally. News & blog The latest from Amazon researchers Amazon Science Blog Technical deep-dives and perspectives from our scientists. News Research milestones and recent achievements. The latest from Amazon researchers Amazon Science Blog Technical deep-dives and perspectives from our scientists. News Research milestones and recent achievements. Collaborations Amazon Research Awards Overview Call for proposals Latest news Research stories Recipients Amazon Nova AI Challenge Overview Rules FAQs Teams Research collaborations Overview Carnegie Mellon University Columbia University Hampton University Howard University IIT Bombay Johns Hopkins University Max Planck Society MIT Tennessee State University University of California, Los Angeles University of Illinois Urbana-Champaign University of Southern California University of Texas at Austin Virginia Tech University of Washington Amazon Research Awards Overview Call for proposals Latest news Research stories Recipients Amazon Nova AI Challenge Overview Rules FAQs Teams Research collaborations Overview Carnegie Mellon University Columbia University Hampton University Howard University IIT Bombay Johns Hopkins University Max Planck Society MIT Tennessee State University University of California, Los Angeles University of Illinois Urbana-Champaign University of Southern California University of Texas at Austin Virginia Tech University of Washington Resources Code and datasets Amazon Nova Try Amazon’s frontier foundation models. Code and datasets Amazon Nova Try Amazon’s frontier foundation models. Careers Careers Explore our open roles. Amazon Scholars Faculty research opportunities on industry-scale technical challenges. Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Careers Explore our open roles. Amazon Scholars Faculty research opportunities on industry-scale technical challenges. Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Search Submit Search ` A decade of mathematical certainty: Reflections on the Automated Reasoning Group Read more ` A new benchmark for evaluating patient-facing health AI agents Read more Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law Read more Amazon News Why AI for good depends on good data Read more Preserving the privacy of AI training data Read more A decade of mathematical certainty: Reflections on the Automated Reasoning Group A new benchmark for evaluating patient-facing health AI agents Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law Why AI for good depends on good data Preserving the privacy of AI training data Customer-obsessed science Research areas Automated reasoning Cloud and systems Computer vision Conversational AI Economics Information and knowledge management Machine learning Operations research and optimization Quantum technologies Robotics Search and information retrieval Security, privacy, and abuse prevention Sustainability From the blog View all Technical deep-dives and perspectives from our scientists. View all How controllers from industrial machinery can coordinate multitask machine learning July 30, 2026 8 min read Instead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. Machine learning Amazon and University of Michigan give robots a sense of touch July 10, 2026 5 min read Robotics Capturing token IDs during agentic interactions for better reinforcement learning July 9, 2026 10 min read Machine learning The fuel of the future is already here: Why TRISO matters June 24, 2026 5 min read Sustainability EC2’s formally verified “isolation engine” provides mathematical assurance of virtual-machine isolation June 10, 2026 7 min read Automated reasoning View all Featured news 34 Amazon Research Awards Build on Trainium recipients announced The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI. AWS and Hopkins Engineering announce database for AI/ML antibody design The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design. 2026 Amazon Nova AI Challenge: Trusted Software Agents track Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale. Amazon launches $68 million AI PhD Fellowship program Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities. Publications View all View all Huddle: Parallel shape assembly using decentralized, minimalistic robots Khai Yi Chin, Tingwei Meng, ZHE CHEN, Daniel Bassett, Yuri Ivanov DARS 2026 2026 We propose a novel algorithm for forming arbitrarily shaped assemblies using decentralized robots. By relying on local interactions, the algorithm ensures there are no unreachable states or gaps in the assembly, which are global properties. The in-assembly robots attract passing-by robots into expanding the assembly via a simple implementation of signaling and alignment. Our approach is minimalistic, requiring Robotics EvoMAS: Evolutionary generation of multi-agent systems Yuntong Hu, Yuting Zhang, Matthew Trager, Yi Zhang, Shuo Yang, Wei Xia, Stefano Soatto ICML 2026 2026 Large language model (LLM)–based multi-agent systems (MAS) show strong promise for complex reasoning, planning, and tool-augmented tasks, but designing effective MAS architectures remains labor-intensive, brittle, and hard to generalize. Existing automatic MAS generation methods either rely on code generation, which often leads to executability and robustness failures, or impose rigid architectural templates Machine learning Knowing when to quit: A principled framework for dynamic abstention in LLM reasoning Hen Davidov, Nachshon Cohen, Oren Kalinsky, Yaron Fairstein, Guy Kushilevitz, Ram Yazdi, Patrick Rebeschini ICML 2026 2026 LLMs utilizing chain-of-thought reasoning often waste substantial compute by producing long, incorrect responses. Abstention can mitigate this by withholding outputs unlikely to be correct. While most abstention methods decide to withhold outputs before or after generation, dynamic mid-generation abstention considers early termination of unpromising reasoning traces at each token position. Prior work has Conversational AI Practical multilingual product title optimization at scale with small language models Bryan Zhang, Stephan Walter, Luca Lomanto, Merve Arinik AMTA 2026 2026 E-commerce product titles often include redundant information that negatively impacts the user experience. Removing repeated words through restructuring and paraphrasing can make titles more concise and improve readability. While large language models can optimize titles, their computational cost makes them impractical for large-scale applications. In this paper, we first analyze the sources of repetition Conversational AI Retrieval-constrained policy optimization for attack technique extraction from cyber threat intelligence Jiayun Zhang, Junshen Xu, Zejun Xie, Yi Fan KDD 2026 Workshop on AI Agent for Information Retrieval (AGENT4IR) 2026 Mapping cyber threat intelligence (CTI) text to MITRE ATT&CK techniques is essential for structured threat analysis, yet manual annotation is costly and does not scale. The ATT&CK taxonomy comprises several hundred attack techniques, and a single CTI pas…