SafePyramid - L1 Rule Dependencies: leaderboard

Rule matching rate on the 1,000 L1 cases, whose policies add exception and conditional rules that modify base rules, from SafePyramid (2026), an in-context policy guardrailing benchmark: a case matches when neither the false-positive nor the false-negative rules exceed a threshold share of the true violations, averaged over thresholds from 0.7 to 1.0. Higher is better.

Metric: Rule matching rate (%) on the 1,000 L1 cases (policies that add exception and conditional rules): per case (a multi-turn conversation plus an in-context safety policy), the predicted set of violated rules matches the ground-truth set when neither false positives nor false negatives exceed (1 - tau) of the true violations; averaged over tau = 0.7, 0.8, 0.9 and 1.0; per-policy evaluation, refused cases excluded; higher is better. Source: arxiv.org. Saturation forecast: Around May 2028. 12 models tracked.

Top models

#ModelScoreOverall rank
1GPT-5.5 (xHigh)56.8#49 (GPT-5.5)
2Hy3-preview (High)55.9#316 (Hy3-preview)
3DeepSeek V4 Pro (Max)54.5#188 (DeepSeek V4 Pro)
4Seed 2.0 Pro (High)54.1#211 (Seed 2.0 Pro)
5Gemini 3.5 Flash (High)53.9#101 (Gemini 3.5 Flash)
6Claude Opus 4.7 (Max)51.1#79 (Claude Opus 4.7)
7Qwen 3.6 Max (Preview) (Thinking)46.5
8Grok 4.3 (High)39.4#333 (Grok 4.3)

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=safepyramid-l1-rule-dependencies · How It Works · Data refreshed daily, snapshot 2026-11-13.