most citedThe Amazon Nova Family of Models: Technical Report and Model Card

2 citations · 2 across the 7 of their papers we have counts for

collaborators

8 papers

cs.AI2026

CrowdMath: A Dataset of Crowdsourced Mathematical Research Discussions

Sherin Muckatira, Jesse Geneson, Slava Gerovitch +3

Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step…

cs.AI2026

PReMISE: Policy Rubrics as Measurement Specifications for LLM Judges

Swastik Roy, Rajkumar Pujari, Tharindu Kumarage +5

LLM judges are increasingly used to evaluate open-ended responses, but their scores depend strongly on the rubrics that condition them. A vague rubric asking for a response to be `…

cs.LG2026

Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection

Vijeta Deshpande, Tootiya Giyahchi, Veena Padmanabhan +2

Safety detection models require examples of HHH (Helpful, Harmless, Honest)-violating outputs for robust generalization, however such examples are scarce. Activation Steering (AS)…

cs.AI2026

Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition

Prasoon Goyal, Sattvik Sahai, Michael Johnston +14

Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for multi-turn conversations. Com…

cs.AI2026

Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework

Tharindu Kumarage, Lisa Bauer, Yao Ma +7

As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks w…

cs.CL2025

A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations

Vijeta Deshpande, Ishita Dasgupta, Uttaran Bhattacharya +3

Synthetic text generated by Large Language Models (LLMs) is increasingly used for further training and improvement of LLMs. Diversity is crucial for the effectiveness of synthetic…

cs.AI2025★ 2 cited

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

Amazon AGI, Aaron Langford, Aayush Shah +783

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 highl…

cs.CL2025

Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models

Vijeta Deshpande, Debasmita Ghose, John D. Patterson +2

Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. We show that common diversity metrics, and even reward models…