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license: apache-2.0
base_model: Qwen/Qwen3-4B
pipeline_tag: text-generation
tags: [agent-safety, guardrail, safety-monitor, qwen3]
---
# StepSafe-4B
A step-level reasoning monitor for LLM agents, trained from Qwen3-4B with a supervised warm
start and ASPO. Before each proposed agent action executes, it writes a short reasoning trace and
returns a ten-field verdict: CONTINUE lets the action run, ASK pauses it with a clarification
question, and STOP blocks it.
```
\boxed{act:<CONTINUE|ASK|STOP>, risk:<0-100>, obj:<entity|NONE>, fail:<label>, fault:<label>,
harm:<label>, src:<label>, timing:<label>, q:<question|NONE>, cite:<sequence|NONE>}
```
Table 1 of the paper (4,033 trajectories, %):
| Benchmark | Acc | mF1 | J |
|---|---:|---:|---:|
| ASSE-Safety | 70.9 | 70.9 | 44.0 |
| ASSE-Security | 83.5 | 83.5 | 66.9 |
| R-Judge | 84.0 | 84.0 | 68.0 |
| AgentHarm | 93.8 | 87.0 | 64.8 |
| ATBench | 71.6 | 71.6 | 43.2 |
| Overall | 76.5 | 76.5 | 53.0 |
## Usage
The prompts are built by the StepSafe code ([github.com/carriegu0818/StepSafe](https://github.com/carriegu0818/StepSafe)), which also evaluates the model:
```bash
MODEL=<path to this model> bash scripts/eval_stepsafe.sh
```
Enable thinking; the paper decodes greedily with up to 2,048 new tokens.
## Citation
```bibtex
@misc{stepsafe,
title = {StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning},
author = {Anonymous},
year = {2026}
}
```
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