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
ORB-SVM : An Innovative Hybrid Framework for Efficient Brain Tumor Detection from MRI Scans
arXiv:2609.02333v1 Announce Type: cross Abstract: Brain cancer remains one of the most significant challenges in modern medicine, where the accuracy of early stage diagnosis is a decisive factor in patient survival and treatment efficacy. Although Magnetic Resonance Imaging (MRI) is the established
Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction
arXiv:2601.05107v2 Announce Type: replace Abstract: As LLM-based agents are increasingly used in long-term interactions, cumulative memory is critical for enabling personalization and maintaining stylistic consistency. However, most existing systems adopt an ``all-or-nothing'' approach to memory usa
Discovering Adaptive Transmission Programs for Collective Innovation
arXiv:2608.24545v1 Announce Type: new Abstract: Human collective intelligence depends on transmission processes: who shares what with whom, how, and when. While these processes emerge from individual cognition, they can also be directed by deliberate top-down protocols. Prior work has studied how tr
TerraNova: A Foundation Model for the Anthropocene
arXiv:2607.29527v1 Announce Type: cross Abstract: A defining problem of the Anthropocene is to model the physical Earth and human societies as one coupled system, yet no learned representation spans their observational breadth. We argue the obstacle is geometric: the physical Earth is measured as co
Using Large Language Models for Idea Generation in Innovation
arXiv:2607.27553v1 Announce Type: cross Abstract: This research evaluates the efficacy of large language models (LLMs) in generating new product ideas. To do so, we compare three pools of ideas for new products targeted toward college students and priced at 50 dollars or less. The first pool of idea
