SteerBench-Work: Benchmarking Agent Steering at Action Boundaries
So, there's this new benchmark called SteerBench-Work that's all about evaluating how long-term LLM agents make steering decisions in professional settings. It really zeroes in on those crucial pre-commit choices at action boundaries, which can lead to significant outcomes like sending emails or handling payments. This benchmark is unique because it's bidirectional and tied to specific incidents, covering areas like developer ops, customer service, finance, law, healthcare, HR, and security. The latest update, v2026-05, includes 106 scenarios based on real public incidents, with a balanced approach to proceed and hold labels. Early results show that models often hesitate on approved actions, which might indicate they're too cautious, potentially limiting their effectiveness in autonomous roles. The main aim is to boost the safety and dependability of agent steering.
Key facts
- SteerBench-Work is a benchmark for evaluating steering decisions in LLM agents.
- It focuses on the pre-commit choice at action boundaries: proceed or hold.
- The benchmark is incident-anchored and bidirectional.
- It covers developer operations, customer service, finance, legal, medical, HR, and security.
- Release v2026-05 contains 106 scenarios anchored in public incidents.
- Scenarios include paired evidence-reversed mirrors and calibration controls.
- Labels are split nearly evenly between proceed and hold.
- Across 30 model conditions, failures run almost entirely in one direction: models wrongly hold authorized actions.
Entities
Institutions
- arXiv