Customer-obsessed science


Research areas
-
June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
Featured news
-
2025Diffusion Policies are effective at learning closed-loop manipulation policies from human demonstrations but generalize poorly to novel arrangements of objects in 3D space, hurting real-world performance. To address this issue, we propose Spherical Diffusion Policy (SDP), an SE(3) equivariant diffusion policy that adapts trajectories according to 3D transformations of the scene. Such equivariance is achieved
-
Amazon Technical Reports2025Nova Premier is Amazon’s most capable multimodal foundation model and teacher for model distillation. It processes text, images, and video with a one-million-token context window, enabling analysis of large codebases, 400-page documents, and 90-minute videos in a single prompt [2]. We present the first comprehensive evaluation of Nova Premier’s critical risk profile under the Frontier Model Safety Framework
-
2025Video summarization aims to generate a condensed textual version of an original video. Summaries may consist of either plain text or a shortlist of salient events, possibly including temporal or spatial references. Video Large Language Models (VLLMs) exhibit impressive zero-shot capabilities in video analysis. However, their performance varies significantly according to the LLM prompt, the characteristics
-
ESREL SRA-E 20252025The rapid rise of generative AI (GenAI) has sparked the sustainability community to explore its potential applications, such as climate impact modeling and renewable energy optimization. However, deploying these GenAIpowered solutions in enterprise environments raises risk concerns. In particular, chatbots and similar GenAI applications face risks of misinformation and disinformation stemming from knowledge
-
KDD 2025 Workshop on AI for Supply Chain2025Effective attribution of causes to outcomes is crucial for optimizing complex supply chain operations. Traditional methods, often relying on waterfall logic or correlational analysis, frequently fall short in identifying the true drivers of performance issues. This paper proposes a comprehensive framework leveraging data-driven causal discovery to construct and validate Structural Causal Models (SCMs).
Academia
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all