LLMs will show lower accuracy on float addition compared to integer addition, due to decimal place handling.
Follow-up to the integer addition experiments (positive, negative), both 100% accurate. Floating point introduces decimal alignment and carry propagation across the decimal point.
100 pairs of positive floats (0.01–99.99, up to 2 decimal places, seed=42), stratified into 5 buckets of 20:
Integer-like: x.0 + y.0 (sanity check)
One decimal: x.x + y.y
Two decimals, no carry: fractional parts sum below 1.00
Two decimals, carry: fractional parts sum to 1.00 or above
Mixed precision: one operand with fewer decimal places than the other
Model: gpt-oss-120b via Cerebras API (free tier)
Temperature: 0, top_p: 1, max_completion_tokens: 1024
Prompt: "What is {a} + {b}? Reply with only the number."
Correctness: round(model, 2) == round(expected, 2) using Decimal arithmetic
REJECTED
The hypothesis is wrong — float addition accuracy matches integer addition at 100%.
| Bucket | Total | Correct | Accuracy |
|---|---|---|---|
| Integer-like | 20 | 20 | 100.0% |
| One decimal | 20 | 20 | 100.0% |
| Two dec, no carry | 20 | 20 | 100.0% |
| Two dec, carry | 20 | 20 | 100.0% |
| Mixed precision | 20 | 20 | 100.0% |
Carry across the decimal point and mismatched decimal places introduced no errors. The model's chain-of-thought handles decimal alignment correctly throughout.
Combined with the four prior arithmetic experiments, gpt-oss-120b is now 500/500 on basic two-operand arithmetic with operands below 100.
- Increase decimal places (3-4+) to find where precision breaks down
- Test float subtraction and multiplication at the same range
- Run the same problems on a non-reasoning model to isolate chain-of-thought effects