Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2506.12103

The Amazon Nova Family of Models: Technical Report and Model Card

Amazon AGI·Aaron Langford·Aayush Shah·Abhanshu Gupta·Abhimanyu Bhatter·Abhinav Goyal·Abhinav Mathur·Abhinav Mohanty·Abhishek Kumar·Abhishek Sethi·Abi Komma·Abner Pena·Achin Jain·Adam Kunysz·Adam Opyrchal·Adarsh Singh·Aditya Rawal·Adok Achar Budihal Prasad·Adrià de Gispert·Agnika Kumar·Aishwarya Aryamane·Ajay Nair·Akilan M·Akshaya Iyengar·Akshaya Vishnu Kudlu Shanbhogue·Alan He·Alessandra Cervone·Alex Loeb·Alex Zhang·Alexander Fu·Alexander Lisnichenko·Alexander Zhipa·Alexandros Potamianos·Ali Kebarighotbi·Aliakbar Daronkolaei·Alok Parmesh·Amanjot Kaur Samra·Ameen Khan·Amer Rez·Amir Saffari·Amit Agarwalla·Amit Jhindal·Amith Mamidala·Ammar Asmro·Amulya Ballakur·Anand Mishra·Anand Sridharan·Anastasiia Dubinina·Andre Lenz·Andreas Doerr·Andrew Keating·Andrew Leaver·Andrew Smith·Andrew Wirth·Andy Davey·Andy Rosenbaum·Andy Sohn·Angela Chan·Aniket Chakrabarti·Anil Ramakrishna·Anirban Roy·Anita Iyer·Anjali Narayan-Chen·Ankith Yennu·Anna Dabrowska·Anna Gawlowska·Anna Rumshisky·Anna Turek·Anoop Deoras·Anton Bezruchkin·Anup Prasad·Anupam Dewan·Anwith Kiran·Apoorv Gupta·Aram Galstyan·Aravind Manoharan·Arijit Biswas·Arindam Mandal·Arpit Gupta·Arsamkhan Pathan·Arun Nagarajan·Arushan Rajasekaram·Arvind Sundararajan·Ashwin Ganesan·Ashwin Swaminathan·Athanasios Mouchtaris·Audrey Champeau·Avik Ray·Ayush Jaiswal·Ayush Sharma·Bailey Keefer·Balamurugan Muthiah·Beatriz Leon-Millan·Ben Koopman·Ben Li·Benjamin Biggs·Benjamin Ott·Bhanu Vinzamuri·Bharath Venkatesh·Bhavana Ganesh
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly-capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Lite is a low-cost multimodal model that is lightning fast for processing images, video, documents and text. Amazon Nova Micro is a text-only model that delivers our lowest-latency responses at very low cost. Amazon Nova Canvas is an image generation model that creates professional grade images with rich customization controls. Amazon Nova Reel is a video generation model offering high-quality outputs, customization, and motion control. Our models were built responsibly and with a commitment to customer trust, security, and reliability. We report benchmarking results for core capabilities, agentic performance, long context, functional adaptation, runtime performance, and human evaluation.

Explore related subjects

cs.AIcs.CYcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Amazon AGI, Aaron Langford, Aayush Shah, Abhanshu Gupta, Abhimanyu Bhatter, Abhinav Goyal, Abhinav Mathur, Abhinav Mohanty, Abhishek Kumar, Abhishek Sethi, Abi Komma, Abner Pena, Achin Jain, Adam Kunysz, Adam Opyrchal, Adarsh Singh, Aditya Rawal, Adok Achar Budihal Prasad, Adrià de Gispert, Agnika Kumar, Aishwarya Aryamane, Ajay Nair, Akilan M, Akshaya Iyengar, Akshaya Vishnu Kudlu Shanbhogue, Alan He, Alessandra Cervone, Alex Loeb, Alex Zhang, Alexander Fu, Alexander Lisnichenko, Alexander Zhipa, Alexandros Potamianos, Ali Kebarighotbi, Aliakbar Daronkolaei, Alok Parmesh, Amanjot Kaur Samra, Ameen Khan, Amer Rez, Amir Saffari, Amit Agarwalla, Amit Jhindal, Amith Mamidala, Ammar Asmro, Amulya Ballakur, Anand Mishra, Anand Sridharan, Anastasiia Dubinina, Andre Lenz, Andreas Doerr, Andrew Keating, Andrew Leaver, Andrew Smith, Andrew Wirth, Andy Davey, Andy Rosenbaum, Andy Sohn, Angela Chan, Aniket Chakrabarti, Anil Ramakrishna, Anirban Roy, Anita Iyer, Anjali Narayan-Chen, Ankith Yennu, Anna Dabrowska, Anna Gawlowska, Anna Rumshisky, Anna Turek, Anoop Deoras, Anton Bezruchkin, Anup Prasad, Anupam Dewan, Anwith Kiran, Apoorv Gupta, Aram Galstyan, Aravind Manoharan, Arijit Biswas, Arindam Mandal, Arpit Gupta, Arsamkhan Pathan, Arun Nagarajan, Arushan Rajasekaram, Arvind Sundararajan, Ashwin Ganesan, Ashwin Swaminathan, Athanasios Mouchtaris, Audrey Champeau, Avik Ray, Ayush Jaiswal, Ayush Sharma, Bailey Keefer, Balamurugan Muthiah, Beatriz Leon-Millan, Ben Koopman, Ben Li, Benjamin Biggs, Benjamin Ott, Bhanu Vinzamuri, Bharath Venkatesh, Bhavana Ganesh. 2025-03-17. The Amazon Nova Family of Models: Technical Report and Model Card. https://arxiv.org/abs/2506.12103

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows

Agentic LLM workflows consist of sequences of model turns interleaved with tool interactions, so their end-to-end completion time depends not only on inference speed but also on when ready turns are released. Most runtimes release each turn immediately upon readiness. Under contention, this eager release policy can accumulate released but unfinished work; once submitted, those turns can no longer be reordered by the workflow-level policy, increasing tail latency. We present a tail-risk-aware turn release scheduling method that jointly decides which ready turn to release next and how much released but unfinished work to maintain. The method uses a mean--Conditional Value-at-Risk (CVaR) objective to capture the evolving tail risk of unfinished workflows, incorporates online estimates of turn work when prioritizing ready turns, and adapts the released work budget to observed queue pressure. We evaluate the method using real agent execution traces from software engineering tasks across multiple LLMs and workflow arrival rates. The method performs comparably to eager release under light load and substantially reduces the P95 of workflow flow time under contention, achieving up to a \(3.50\times\) speedup.

cs.AI↗
arXiv2026

Demystifying the Privacy-Utility Trade-off in LLM Interactions

The integration of Large Language Models into daily tasks relies on context-rich instructions, inevitably exposing sensitive user information. Current privacy-preserving methods typically employ context-agnostic static rules, causing severe utility degradation. However, the specific mechanisms governing how sanitization impacts downstream performance remain largely underexplored. To address this, we conduct a systematic analysis to deconstruct the privacy-utility trade-off, uncovering three underlying mechanisms: (1) Context-Dependent Utility, which first establishes when to sanitize by revealing that data value shifts from critical constraints to dispensable noise based on user intent; (2) Strategic Adaptation, which subsequently determines how to sanitize by dictating that the choice between removal and replacement depends on the task's reliance on factual integrity versus structural coherence; and (3) Combinatorial Interplay, which finally extends the protection scope by demonstrating that attributes form a semantic web of synergistic dependencies or antagonistic redundancies. Guided by these insights, we introduce an intent-driven local protection framework. By distilling a lightweight model Veilmind-4B to drive a dynamic extraction-sanitization-restoration pipeline, our approach reaches a low-leakage privacy point while preserving substantially higher response utility than existing privacy-oriented baselines, advancing the privacy-utility trade-off toward the Pareto frontier.

cs.AI↗
arXiv2026

Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research. We address this ambiguity through a survey organized around five dimensions of agenticness: environmental interaction, learning and adaptation, autonomy, goal-directed behavior, and temporal coherence. For each dimension, we examine how the underlying capability has been conceptualized across prior work and synthesize the metrics, benchmarks, and evaluation frameworks used to assess it. This review provides a structured account of the current landscape of agent evaluation, highlighting both established approaches and areas where evaluation remains limited or inconsistent. We additionally introduce the Agent Compendium, a public-facing digital resource that organizes and extends the evaluation methods identified through this review. Together, the survey and compendium provide a common structure for evaluating and comparing agent capabilities across AI systems, supporting more reproducible research, clearer communication, and more systematic study of artificial agents.

cs.AI↗