TrueMath

A 1-layer TrueACT model trained to generate chain-of-thought reasoning for arithmetic expressions with +, -, *, and parentheses.

Trained from scratch on purely random binary-tree expressions. Every non-root subexpression is parenthesized, forcing the model to learn step-by-step reduction of nested arithmetic.

Performance

Test	Accuracy
Fixed 12-case benchmark	12/12 (100%)
Random 500 expressions	91.6%

Errors are exclusively multi-digit arithmetic mistakes (e.g., 42×88=3524). The model's structural reasoning (parenthesis resolution, operator precedence, chain-of-thought decomposition) is near-perfect.

Files

File	Description
model.pt	TorchScript-traced model for inference (no Python source needed)
checkpoint.pt	Original PyTorch checkpoint (requires the TrueACT architecture code to load)
infer.py	Standalone inference script
plot.py	Generate the training curves figure from the training log

Usage

# Single prompt
python infer.py model.pt '((5*5)+(10*2))='

# Interactive mode
python infer.py model.pt
Prompt > (1+2)*3=

The model expects a prompt ending with = and generates the chain-of-thought steps:

Input:  ((5*5)+(10*2))=
Output: ((5*5)+(10*2))=(25+(10*2))=(25+20)=45

Requirements

• Python 3.10+
• PyTorch 2.0+

No other dependencies.