# Addition Accuracy on Negative Floats (gpt-oss-120b)

> LLMs will show lower accuracy on float addition involving negative numbers, combining decimal handling with sign handling challenges.

Date: 2026-01-18



## Test

Combines two challenges that individually produced no errors: decimal handling ([positive floats](/scratchpads/addition-accuracy-positive-floats)) and sign handling ([negative integers](/scratchpads/simple-addition-negative-integers)). Tests whether the combination introduces errors.

100 addition problems with negative floats (absolute values 0.01–99.99, up to 2 decimal places, seed=42), stratified into 4 cases:
- **One negative, positive result** (30 pairs)
- **One negative, negative result** (30 pairs)
- **One negative, zero result** (10 pairs): b = -a
- **Both negative** (30 pairs)

- 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


## Result

**REJECTED**

100% accuracy across all 100 problems. The combination of decimal handling and sign handling produced zero errors.

| Case | Total | Correct | Accuracy |
|------|:-:|:-:|:-:|
| One negative, positive result | 30 | 30 | 100.0% |
| One negative, negative result | 30 | 30 | 100.0% |
| One negative, zero result | 10 | 10 | 100.0% |
| Both negative | 30 | 30 | 100.0% |

Combined with all prior arithmetic experiments, gpt-oss-120b is now 600/600 on basic two-operand arithmetic (addition and multiplication, integers and floats, positive and negative) with operands below 100 in absolute value.


## Next

1. Test float subtraction and division with negative operands
2. Scale up decimal places to find precision limits
3. Run on a non-reasoning model to see if chain-of-thought is carrying the result