PolicyShiftBench - Adaptive Split: leaderboard

PolicyShiftBench (2026) tests policy-adaptive image guardrails: a model must block or pass an image according to the runtime policy it is given, and the same image flips labels under different policies. F1 in percent on the 1,000-instance split whose policies come from the training policy families; higher is better.

Metric: F1 of the block decision (%; policy-conditioned image guardrailing: decide whether an image violates the supplied runtime policy bundle (one policy variant per risk category, seven categories); Adaptive split, 1,000 instances over 130 images under 16 policies from the training policy families; mean of three evaluations). Source: arxiv.org. Saturation forecast: Around February 2027. 19 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Flash (Preview)67#144
2Qwen 3.5 35B A3B (Non-reasoning)63.7#482 (Qwen 3.5 35B A3B)
3Qwen 3.5 35B A3B (Thinking)63.5#482 (Qwen 3.5 35B A3B)
4Qwen 3.5 4B (Non-reasoning)59#1175 (Qwen 3.5 4B)
5GPT-5.457.8#123
6Claude Sonnet 4.652.5#161
7Qwen 3.5 4B (Thinking)37.9#1175 (Qwen 3.5 4B)
8Qwen 2.5 VL 7B29#1787
9Qwen 3.5 2B (Non-reasoning)18.2#4810 (Qwen 3.5 2B)
10Qwen 3.5 0.8B (Non-reasoning)3.5#8081 (Qwen 3.5 0.8B)

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

Interactive version: theaggregate.ai/benchmark?slug=policyshiftbench-adaptive-split · How It Works · Data refreshed daily, snapshot 2026-11-13.