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Core Verification Engine

Mathematical Truth,
Not Statistical Probability.

Stop treating math as a language problem. QWED parses natural language into symbolic logic and executes it using a hardened SymPy kernel. It doesn't predict numbers; it solves for absolute truth.

Standard LLM
Prompt
"Calculate the monthly payment on a $500k loan at 7% interest for 30 years."
Output
$3,320.00 (Hallucinated)
Failure: Probabilistic token prediction drifted from the amortization formula.
With QWED
Verification Layer
>> sympy.finance.amortization(500000, 0.07/12, 360)
$3,326.51 ✓ Verified Logic

Why do LLMs fail at math?

Large Language Models like GPT-4 and Claude are "Next Token Predictors." They don't actually perform calculation; they remember arithmetic patterns from their training data.

The Memorization Trap

An LLM knows that "2 + 2 =" is usually followed by "4". But ask it to calculate the cube root of 8,492, and it will confidently hallucinate a number that looks right but is mathematically wrong.

Floating Point Drift

Even when they try to calculate, LLMs often struggle with precision. A 0.01 deviation in a financial report or an engineering load calculation is catastrophic.

No Logic Constraints

LLMs lack an internal consistent world model. They can say "A > B" and "B > C" but then fail to conclude "A > C" in complex word problems.

How the Engine Works

A deterministic pipeline for absolute precision.

1

Intent Parsing

Extracts mathematical entities (numbers, units, operations) from natural language prompts.

2

AST Generation

Constructs an Abstract Syntax Tree representing the problem as pure logic, stripping away linguistic noise.

3

Symbolic Kernel

Executes the logic using SymPy (Python) or Z3. Solves integrals, derivatives, and equations algebraically.

4

Formatting

Converts the result back into human-readable text, JSON, or LaTeX for rendering.

The Liability Shield

In regulated industries, an AI hallucination is not just a bad answer. It can create legal, financial, and operational exposure. QWED adds verification checks and auditability before those results reach production workflows.

FinTech Compliance

Truth in Lending Act (TILA) requires 100% accurate APR disclosures. Relying on raw GPT-4 for loan estimates exposes you to massive regulatory fines.

EdTech Integrity

You cannot teach math with "approximate" answers. QWED ensures your AI tutor provides the correct steps and final solution, every time.

Engineering Safety

Validate unit conversions (e.g., PSI to Pascals) and load calculations. A decimal point error in structural engineering can be fatal.

Engine Capabilities

Arithmetic & Algebra
Linear/Quadratic equations, polynomials, factorization.
Calculus
Symbolic integration, differentiation, limits, series expansion.
Discrete Math
Combinatorics, permutations, graph theory logic.
Financial Math
Amortization, CAGR, NPV, IRR calculations.
Physics & Units
Dimensional analysis and unit conversion validation.
LaTeX Generation
Output formatted for MathJax/KaTeX rendering.

Ready to eliminate math errors?