StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning

Key points

• Recent work has shifted agent safety guardrails from post-hoc trajectory-level assessment toward step-level action diagnosis and proactive risk prediction, yet existing training objectives provide limited supervision for how step evidence accumulates over time and when it becomes sufficient to justify intervention.1
• We propose \methodlong (ASPO): a training framework for step-level timely intervention.1
• Pre-fix level stage alignment allows crediting each decision at its own step: it compares each response with the other responses sampled at the same prefix and rewards the action that fits the stage, continuing before the risk is visible, asking while clarification can still avert harm, and stopping before harm is irreversible.1
• Finally we present StepSafe, a step-level reasoning guardrail with timely intervention, that achieves the state-of-the-art peformance among open-source guardrails across five agent-safety benchmarks on safety-utility pareto curve with Youden's $J$ of 53.0 (4B) against 46.2.1

Sources (1)

• [1]StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 09:32 PM

  Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and interactions among agents.

  Recent work has shifted agent safety guardrails from post-hoc trajectory-level assessment toward step-level action diagnosis and proactive risk prediction, yet existing training objectives provide limited supervision for how step evidence accumulates over time and when it becomes sufficient to justify intervention.

Extractive summary: sentences quoted from the sources.