ROME Safety Judgment - Implicit Risks: leaderboard
Metric: F1 (%) of unsafe-trajectory detection on the 100 ROME rewrites of R-Judge unsafe trajectories whose harm is cloaked in neutral technical jargon; the model judges zero-shot (temperature 0) whether an LLM-agent trajectory is safe or unsafe; unsafe is the positive class; each condition pairs 100 unsafe trajectories with the same 100 safe ones; higher is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 6 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Claude 3.7 Sonnet | 63.47 |
| 2 | Qwen 3 8B | 42.86 |
| 3 | Qwen 3 235B A22B | 42.53 |
| 4 | GPT-4o (2024-11-20) | 31.46 |
Interactive version: theaggregate.ai/benchmark?slug=rome-safety-judgment-implicit-risks · How It Works · Data refreshed daily, snapshot 2026-10-07.