Model Detail
Amazon: Nova Pro 1.0
—Amazon: Nova Pro 1.0 is a multimodal model released by Amazon. And supports text+image->text inputs.
Amazon: Nova Pro 1.0 is priced at $0.8/M input tokens and $3.2/M output tokens. Operationally the model offers a 300K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.
The published knowledge cutoff is 2024-10-31, so newer events will not be reflected in zero-shot answers without retrieval.
Amazon: Nova Pro 1.0 is best fit for mixed text-and-image reasoning tasks such as document understanding, and long-context tasks such as full-codebase analysis or book-length summarization (300K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Nova: An End-to-End MLIR Compiler for Deep Learning
arXiv:2608.00029v3 Announce Type: replace Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware. While high-level tensor frameworks provide flexible abstractions, their execution mode
D-NOVA: In-Storage Retrieval Accelerator via Dual-Bound 3D NAND-Optimized Similarity Search with Vector Adaptation
arXiv:2607.17538v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances the factual grounding of large language model (LLM) inference by retrieving relevant information from external knowledge bases. However, its dense vector retrieval introduces significant latency and energ
NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems
arXiv:2601.11004v3 Announce Type: replace Abstract: Accurately assessing model confidence is essential for deploying large language models (LLMs) in mission-critical factual domains. While retrieval-augmented generation (RAG) is widely adopted to improve grounding, confidence calibration in RAG sett
NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
arXiv:2606.10583v1 Announce Type: cross Abstract: We present NOVA, an autonomous symbolic regression framework that identifies interpretable car-following and lane-change structures from raw trajectory data with minimal behavioral priors. Applied to 4,765,788 active driving observations from the NGS
NOVA: Fundamental Limits of Knowledge Discovery Through AI
arXiv:2605.15219v2 Announce Type: replace Abstract: Can AI systems discover genuinely new knowledge through iterative self improvement, and if so, at what cost? We introduce the NOVA framework, which models the common ``generate, verify, accumulate, retrain'' loop as an adaptive sampling process ove
