Model Detail
AURA
—AURA is a code generation model with 8B parameters released by aurateam. The model is registered under the video-text-to-text pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
AURA ships with 8B parameters. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
AURA is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. 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.
AURA: Angular Update Rate Adaptation for training complex-valued neural networks
arXiv:2609.25914v2 Announce Type: replace Abstract: Complex-valued neural networks (CVNNs) are increasingly adopted for complex-valued data; however, they are often trained with first-order optimizers inherited from the real-valued case. The efficiency of these methods depends largely on the step si
AURA: Uncertainty-Routed Activation Editing for Acoustic Grounding in Speech Foundation Models
arXiv:2609.23979v1 Announce Type: cross Abstract: Attention encoder-decoder (AED) Speech Foundation Models achieve strong ASR performance but can generate acoustically unsupported text when inputs contain no speech, weak acoustic evidence, or unreliable transcription. We propose AURA: Activation-edi
AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection
arXiv:2609.19873v1 Announce Type: new Abstract: Email spam and phishing attacks remain a critical security threat. Adversaries increasingly exploit large language models to craft contextually convincing malicious messages, and existing spam detection systems often struggle to keep pace. Generalizati
AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale
arXiv:2609.16625v1 Announce Type: cross Abstract: How and why does a recommender system fail the users it serves? Oftentimes, practitioners are left to improve their algorithms based on a combination of feedback from stakeholder teams, domain expertise, and insights from data analyses. Yet the nuanc
AURA: Unified Multimodal Framework for Conversational Music Editing
arXiv:2609.14344v1 Announce Type: cross Abstract: Instruction-guided music editors typically process each request independently, limiting their ability to support workflows in which users progressively refine a track. We introduce AURA, a unified multimodal framework for conversational music editing
AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents
arXiv:2606.05557v1 Announce Type: new Abstract: A situated query like "where is Lin Wei?" often encodes more than its literal content: the user may also want to know whether Lin Wei is free, in a good mood, or worth interrupting now. Standard tool-use agents answer the literal question and stop. AUR
