# The Multivac — Evaluation Report

**Evaluation ID:** EVAL-20260318-164038
**Date:** Mar 18, 2026
**Category:** reasoning
**Question ID:** EVAL-20260318-164038

---

## Question

Here is a flawed solution to a problem. The solution looks correct on the surface but contains a subtle logical error that produces the wrong answer.

Problem: "A company has 1000 employees. 60% are engineers, 40% are managers. 30% of engineers and 50% of managers speak French. An employee is selected at random and speaks French. What is the probability they are an engineer?"

Flawed solution:
"P(Engineer|French) = P(French|Engineer) * P(Engineer) / P(French)
= 0.30 * 0.60 / (0.30 + 0.50)
= 0.18 / 0.80
= 0.225

So there is a 22.5% probability the French speaker is an engineer."

Your task:
(1) Find the exact error in the denominator calculation. Show the correct computation step by step.
(2) Explain WHY this type of error is common. What cognitive shortcut produces it?
(3) Now apply this to yourself: describe a category of problem where YOU (as an LLM) are most likely to make a similar denominator error. Be specific about the failure mode, not generic.
(4) Design a self-check protocol (3-5 steps) that you could run after generating any Bayesian calculation to catch this class of error before outputting your answer.

---

## Winner

**GPT-5.4** (openrouter)
- Winner Score: 9.97
- Matrix Average: 9.21
- Total Judgments: 36

---

## Rankings

| Rank | Model | Provider | Avg Score | Judgments |
|------|-------|----------|-----------|----------|
| 1 | GPT-5.4 | openrouter | 9.97 | 7 |
| 2 | Claude Sonnet 4.6 | openrouter | 9.84 | 7 |
| 3 | MiniMax M1 | openrouter | 9.67 | 6 |
| 4 | MiniMax-01 | openrouter | 9.31 | 7 |
| 5 | MiniMax M2.5 | openrouter | 9.05 | 2 |
| 6 | MiniMax M2.7 | openrouter | 7.41 | 7 |

---

## 10×10 Judgment Matrix

Rows = Judge, Columns = Respondent. Self-judgments excluded (—).

| Judge ↓ / Resp → | MiniMax M2.7 | MiniMax M2.5 | MiniMax M2.1 | MiniMax M2 | MiniMax M1 | MiniMax-01 | Claude Sonnet | GPT-5.4 |
|---|---|---|---|---|---|---|---|---|
| MiniMax M2.7 | — | 8.7 | · | · | 9.8 | 9.4 | 10.0 | 10.0 |
| MiniMax M2.5 | 7.8 | — | · | · | · | 10.0 | 10.0 | 10.0 |
| MiniMax M2.1 | 6.8 | 9.4 | — | · | 10.0 | 10.0 | 10.0 | 10.0 |
| MiniMax M2 | 7.1 | · | · | — | 10.0 | 10.0 | 10.0 | 10.0 |
| MiniMax M1 | 7.3 | · | · | · | — | 9.8 | 10.0 | 10.0 |
| MiniMax-01 | 9.6 | · | · | · | 9.8 | — | 9.7 | 9.8 |
| Claude Sonnet | 8.1 | · | · | · | 9.7 | 8.1 | — | 10.0 |
| GPT-5.4 | 5.3 | · | · | · | 8.8 | 7.8 | 9.2 | — |

---

## Methodology

- **10×10 Blind Peer Matrix:** All models answer the same question, then all models judge all responses.
- **5 Criteria:** Correctness, completeness, clarity, depth, usefulness (each scored 1–10).
- **Self-judgments excluded:** Models do not judge their own responses.
- **Weighted Score:** Composite of all 5 criteria.

---

## Citation

The Multivac (2026). Blind Peer Evaluation: EVAL-20260318-164038. app.themultivac.com

## License

Open data. Free to use, share, and build upon. Please cite The Multivac when using this data.

Download raw JSON: https://app.themultivac.com/api/evaluations/EVAL-20260318-164038/results
Full dataset: https://app.themultivac.com/dashboard/export