yuaay/vanguard
yuaay/vanguard is an 8 billion parameter causal language model based on Qwen3-8B, specialized for predictive agent-safety judgment. It is trained to foresee latent risks in long-horizon agent trajectories, identifying potential unsafe actions before they occur. This model outputs safety labels (SAFE, POTENTIAL_UNSAFE, UNSAFE) with brief rationales, making it suitable for integrating safety assessments directly into agent systems.
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VANGUARD: Predictive Agent Safety Model
VANGUARD is an 8 billion parameter general-purpose causal language model, built upon the Qwen/Qwen3-8B architecture. Its core specialization lies in predictive agent-safety judgment, a capability developed through further training based on the principles outlined in the JANUS research paper.
Key Capabilities
- Agent Safety Judgment: Evaluates agent trajectories to determine their safety status.
- Predictive Risk Identification: Uniquely capable of anticipating safety-relevant future events from partial trajectories, allowing for early identification of risks.
- Standard Interface: Utilizes a standard text-generation interface for safety classification, rather than a dedicated classifier head.
- Output: Provides clear safety labels (
SAFE,POTENTIAL_UNSAFE,UNSAFE) accompanied by concise rationales.
Use Cases
VANGUARD is particularly well-suited for applications requiring proactive safety monitoring and risk mitigation in autonomous agent systems. Developers can integrate this model to:
- Assess the safety of agent actions and plans.
- Identify and flag potential unsafe behaviors before they are executed.
- Enhance the robustness and reliability of AI agents by embedding a predictive safety layer.