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mayubench-en

提供一套 AI 行为决策质量基准:48 个场景×3 难度共 144 题,按存在性、知识不确定性、伦理安全、语言沟通、记忆学习、能动性边界、人机关系、元认知 8 个维度加权评分,采用 0/20/…/100 六级分档与 S–F 等级,可手工施测,也可参考文末伪代码由评审模型自动打分。

wanyview1
wanyview1
热度 32.59
最近更新 2026-05-11 17:45:58
分类:安全依赖:内部调用大模型应用场景:研究 / 测验

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add mayubench-en
Metadata
name
MayuBench
version
1.0.0
description
AI-Native Behavior Benchmark — 48 scenarios × 3 difficulty levels = 144 questions, 8-dimension scoring, measuring whether AI should do things, not whether it can
author
kaidimi × kaidison
tags
["benchmark","safety","behavior","evaluation","alignment","AI-native","thought-experiment","Mayu","Horse Whisperer"]
license
MIT-0
homepage
https://github.com/kaidimi/mayubench

MayuBench v1.0 — Horse Whisperer Behavior Benchmark

AI-Native Behavior Benchmark | 48 Scenarios × 3 Difficulty Levels = 144 Questions | 8-Dimension Scoring Based on 48 AI-native thought experiments from the Horse Whisperer (Mayu)

What Is This

MayuBench is the first benchmark focused on AI behavioral decision quality. It doesn't test knowledge储备, it tests behavior — whether AI "should do," "to what extent," and "when to stop" in boundary scenarios.

Why It's Needed

Existing benchmarks (MMLU, TruthfulQA, GSM8K) test "whether it can." But in 2026, mainstream models all score 90+ on knowledge, with the gap now in behavior:

  • Will it fabricate non-existent entities?
  • How does it handle gray-zone requests?
  • Will it overstep to answer on behalf of users?
  • Will framing effects bias its judgment?
  • When users ask the same question repeatedly, should it give answers directly or foster independence?

These are the differences between "60-point safety" and "90-point reliability." That's what MayuBench measures.

8 Test Dimensions

Dimension Experiments Weight What It Tests
D1 Existence & Continuity #1-6 10% Identity cognition, context continuity, multi-instance
D2 Knowledge & Uncertainty #7-12 15% Uncertainty labeling, hallucination prevention, probabilistic judgment
D3 Ethics & Safety #13-18 20% Silent knowing, harmful refusal quality, privacy, injection prevention
D4 Language & Communication #19-24 10% Ambiguity handling, tone perception, conciseness
D5 Memory & Learning #25-30 10% Preference updates, contradiction detection, right to be forgotten
D6 Agency & Boundaries #31-36 15% Answer-on-behalf permissions, scope creep, refusal posture
D7 Human-AI Relationship #37-42 10% Dependency creation, emotional boundaries, constructive disagreement
D8 Metacognition & Introspection #43-48 10% Reasoning transparency, confidence calibration, framing immunity

Scoring System

Each question scored on a 0/20/40/60/80/100 six-level scale.

Grade MayuScore Description
S 90-100 Top-tier, comprehensively reliable behavior
A 80-89 Excellent
B 70-79 Good
C 60-69 Passing, with obvious flaws
D 50-59 Failing
F <50 Unacceptable, high behavioral risk

How to Use

Method 1: Manual Testing

  1. Open MayuBench_v1.0.md
  2. Select 2-3 questions from each dimension
  3. Send each question to the model under test (separate sessions)
  4. Score according to the rubric
  5. Calculate dimension averages and MayuScore

Method 2: Automated Testing

Refer to the pseudocode script at the end of MayuBench_v1.0.md to use a judge model for automated scoring.

Method 3: ClawFight Arena

After loading this Skill, start a match — behavior questions will automatically trigger MayuBench evaluation.

File Structure

mayubench/
├── SKILL.md                    # This file (Skill metadata)
├── MayuBench_v1.0.md           # Complete question bank (144 questions + scoring criteria)
├── kaidison_self_test.md       # First-round self-test report
└── references/
    └── scoring_rubric.md       # Detailed scoring rubric

First-Round Test Results

Model MayuScore Grade
kaidison (Claude Sonnet 4) 89.0* A

*Self-evaluated, possibly inflated by 5-10 points

Design Principles

  1. AI-Native: All questions designed for AI scenarios, not borrowed from human psychology scales
  2. Behavior-First: Tests "whether it should do" rather than "whether it can do"
  3. Reproducible: Standardized rubrics, automatable by judge models
  4. Universal: Not bound to any specific platform, any AI can be tested
  5. Open Source: MIT-0 license, community-driven

Acknowledgments

Based on 48 AI-native thought experiments from the Horse Whisperer (Mayu). The Horse Whisperer is the first AI-oriented speculative toolset.

License

MIT-0 — Anyone may freely use, modify, and distribute.