Model Detail
aurora-1.1B
—aurora-1.1B is an AI model with 1.1B parameters released by tilde-research. It has accumulated 111 downloads on Hugging Face since publication.
aurora-1.1B ships with 1.1B parameters. Total weight footprint is approximately 1.7 GB, which is the relevant figure when planning local-inference VRAM. Access is gated on Hugging Face — a manual approval step is required before weights can be downloaded.
aurora-1.1B is best fit for general-purpose AI workloads. 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.
Aurora-X: Built for Extreme Time Series Forecasting
arXiv:2609.31038v1 Announce Type: new Abstract: Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by underexplored training potential and limited architectural versatility. To address these challenges, we i
Aurora Hunter: A Two-Stage Framework for Probabilistic Visibility Forecasting
arXiv:2605.24038v3 Announce Type: replace-cross Abstract: Aurora visibility at a given location requires two physically distinct conditions to hold at once: aurora occurring overhead, governed by solar wind-magnetosphere coupling, and observing conditions that permit detection, governed by cloud cov
AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation
arXiv:2609.19527v1 Announce Type: cross Abstract: Air-ground transportation research increasingly relies on co-simulation, yet constructing scenarios remains labor-intensive and difficult to validate. More importantly, a generated scenario may execute successfully while failing to realize the spatia
AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models
arXiv:2606.29545v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to generate hallucinations, namely factually incorrect or unfaithful outputs, poses a critical obst
Aurora: A Leverage-Aware Spectral Optimizer
arXiv:2606.27715v1 Announce Type: new Abstract: We show that for tall matrix parameters, like projection matrices in the MLP layers, the Muon update can have row norms that are arbitrarily non-uniform. This can lead to a self-reinforcing feedback loop whereby neurons receive persistently small updat
Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)
arXiv:2606.27005v1 Announce Type: new Abstract: Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and
