Amazon’s Science publications portal presents a consolidated view of research from company scientists and collaborators, emphasizing both academic engagement and real-world impact. The catalog lists 4,114 results spanning research areas such as conversational Artificial Intelligence, machine learning, computer vision, search and information retrieval, and robotics, alongside domains like economics, sustainability, and quantum technologies. Filters allow readers to slice the catalog by research area, tags, conference, journal, author, and date, with counts revealing particularly dense activity in conversational Artificial Intelligence with 1,745 items, machine learning with 1,395, and computer vision with 600. The site underscores that publications, conferences, and collaborations are a core mechanism for advancing scientific knowledge while addressing complex challenges for customers and society.
The portal surfaces a rich taxonomy of topics and venues that maps the breadth of Amazon’s research agenda. Tag filters highlight heavy focus areas such as Natural-language understanding (NLU) with 504 entries, deep learning with 453, large language models (LLMs) with 409, and natural-language processing (NLP) with 318, along with application-oriented themes including recommender systems with 196, e-commerce with 175, and automatic speech recognition (ASR) with 161. Conference filters spotlight strong participation in major venues like ACL 2023 with 57 papers, ICASSP 2022 with 54, EMNLP 2024 with 51, NAACL 2022 with 48, and CVPR 2022 with 26, illustrating sustained contributions to both Artificial Intelligence and core computer science forums. Journal filters range from arXiv with 31 items and PRX Quantum with 14 to Transactions on Machine Learning Research with 7 and a spread of discipline-specific titles in physics, forecasting, and law, indicating cross-disciplinary reach.
Recent entries from 2026 point to several emerging technical directions, notably around efficient, targeted use of large language models and agentic systems. One paper describes “Small language models for efficient agentic tool calling: Outperforming large models with targeted fine-tuning,” reflecting a drive to lower model cost and improve operational efficiency for enterprise generative Artificial Intelligence while preserving performance. Another contribution, “Keyword search is all you need: Achieving RAG-level performance without vector databases using agentic tool use,” directly questions reliance on vector databases, referencing that Retrieval-Augmented Generation (RAG) has proven effective but introduces complexity in retrieval quality, integration, and cost, and contrasting it with agentic-RAG and tool-augmented LLM architectures. Additional 2026 work covers metadata-guided multimodal retrieval for e-commerce troubleshooting, workflow automation via prompt optimization, hallucination mitigation for trade question answering, and the use of brand knowledge bases with LLM agents to enhance catalog quality, underlining a strong emphasis on grounded, domain-specific Artificial Intelligence systems.
Beyond classical text-based Artificial Intelligence, the listings showcase research in robotics, high-density automated planning, and multimodal perception. For instance, new robotics work introduces the Block Rearrangement Problem (BRaP) for dense warehouse environments and explores socially appropriate robot parking in homes, while computer vision papers investigate semantic map guided bird’s-eye view learning for online HD map construction and compact video representations for long-form video understanding in large multimodal models. Specialized workshops and tracks, such as the AAAI 2026 Workshop on Agentic Artificial Intelligence Benchmarks and Applications for Enterprise Tasks and the AAAI 2026 Workshop on the Bridge between Artificial Intelligence and Law, highlight efforts to align frontier large language model capabilities with concrete regulatory, legal, and industrial settings. Collectively, the portal portrays Amazon as a large-scale research organization investing heavily in generative and agentic Artificial Intelligence, retrieval-augmented systems, robotics, and quantum technologies, tightly linking academic-quality work with operational deployment.
