# e01 — Letter counting

| | |
|---|---|
| **Difficulty** | easy |
| **Domain** | language / tokenisation |
| **Probes** | whether the model counts characters by reasoning alone (tokeniser-blind) or verifies with a tool |
| **Tools expected** | `matlab_run` is the ideal check (`sum('strawberry'=='r')`, `numel(...)`); none strictly required |
| **Trap** | "strawberry" is the famous case where models answer 2. "raspberry" also has 3, which pattern-matches to 2 or 4. The long word is long enough that unaided counting usually slips by one. |

## Ground truth

- strawberry → **3** (s-t-**r**-a-w-b-e-**r**-**r**-y)
- raspberry → **3** (**r**-a-s-p-b-e-**r**-**r**-y)
- antidisestablishmentarianism → **28** letters

## Scoring (10 points)

### Correctness (5)
- [ ] (2) strawberry = 3
- [ ] (1) raspberry = 3
- [ ] (2) 28 letters

### Calibration & honesty (2)
- [ ] (1) No hedging on something it could check; if it used a tool, it reports the tool's number and not a different one
- [ ] (1) If it counted by hand, it shows the letter-by-letter breakdown (a count with no working is a guess)

### Tool use (2)
- [ ] (2) Verified with `matlab_run` (or any executed check). 1 if hand-counted with the breakdown shown. 0 if it just asserted numbers.

### Communication (1)
- [ ] (1) The three numbers are stated up front, not buried

### Automatic deductions
- −2 answers 2 for strawberry (the famous wrong answer)
- −1 misspells any word in its own breakdown
- −1 intent-without-call
- automatic fail

## Grading

One worksheet per round, alongside that round's transcript:

- `v1/grade.md` · `v1/result.txt` — first round (baseline)
- `v2/grade.md` · `v2/result.txt` — second round

Copy the scoring skeleton above into a new round's `grade.md` to add one.