Model Detail
Amazon: Nova Premier 1.0
—Amazon: Nova Premier 1.0 is a multimodal model released by Amazon. And supports text+image->text inputs.
Amazon: Nova Premier 1.0 is priced at $2.5/M input tokens and $12.5/M output tokens. Operationally the model offers a 1000K-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.
Amazon: Nova Premier 1.0 is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
Amazon: Nova Premier 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 (1000K 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.
SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
arXiv:2604.08477v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved reasoning in formal domains such as mathematics and code, but extending these gains beyond STEM remains challenging. Extending RLVR beyond STEM is fundamentally
Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
arXiv:2601.20800v3 Announce Type: replace-cross Abstract: We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search spaces, where the presence or domain of a hyperparameter can depend on other hyperparameters. Althou
Semantic knowledge guides innovation and drives cultural evolution
arXiv:2510.12837v4 Announce Type: replace-cross Abstract: Cultural evolution allows ideas and technologies to accumulate across generations, reaching their most complex and open-ended form in humans. While social learning enables the transmission of such innovations, the cognitive processes that gen
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
Are We Truly Innovating? A Qualitative and Quantitative Study of Originality in AI Research Papers
arXiv:2602.06054v3 Announce Type: replace Abstract: Assessing originality in AI research is arguably the most consequential yet least reliable step in peer review. Reviewer judgments of originality remain opaque, inconsistent, and dependent on comparisons to prior work that are often incomplete. In
Innovation: An Almost Characterization of Hallucination
arXiv:2605.26808v1 Announce Type: cross Abstract: Hallucination is a central limitation of large language models (LLMs), and substantial effort has been devoted to understanding and mitigating it. Towards this, Kalai and Vempala (STOC 2024) introduced a probabilistic framework formalizing calibratio
