PluraMath extends PolyMath with human-validated math problems in 18 mid-to-extreme low-resource languages and benchmarks 27 reasoning LLMs, finding a persistent high- vs low-resource performance gap.
The Amazon Nova Family of Models: Technical Report and Model Card
2 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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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.
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2026 2representative citing papers
Schema-First Retrieval embeds catalog metadata rather than rows and uses parallel retrieval plus reranking to raise table and column recall and cut SQL execution errors on three benchmarks.
citing papers explorer
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PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages
PluraMath extends PolyMath with human-validated math problems in 18 mid-to-extreme low-resource languages and benchmarks 27 reasoning LLMs, finding a persistent high- vs low-resource performance gap.
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Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics
Schema-First Retrieval embeds catalog metadata rather than rows and uses parallel retrieval plus reranking to raise table and column recall and cut SQL execution errors on three benchmarks.