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), which also evaluates the model:

MODEL=<path to this model> bash scripts/eval_stepsafe.sh

Enable thinking; the paper decodes greedily with up to 2,048 new tokens.

Citation

@misc{stepsafe,
  title  = {StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning},
  author = {Anonymous},
  year   = {2026}
}