mayubench-en

SKILL.md

---
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.