qwed 2.3.0

The Deterministic Verification Protocol for AI - 8 verification engines for math, logic, code, SQL, facts, images, and more.

QWED Protocol

Model Agnostic Verification Layer for AI

QWED Verification - Production-grade deterministic verification layer for Large Language Models. Works with ANY LLM - OpenAI, Anthropic, Gemini, Llama (via Ollama), or any local model. Detect and prevent AI hallucinations through 8 specialized verification engines. Your LLM, Your Choice, Our Verification.

Don't fix the liar. Verify the lie.
QWED does not reduce hallucinations. It makes them irrelevant.

If an AI output cannot be proven, QWED will not allow it into production.

🌐 Model Agnostic: Local ($0) • Budget ($5/mo) • Premium ($100/mo) - You choose!

💖 Support QWED Development:

Quick Start · 🆕 QWEDLocal · The Problem · The 8 Engines · 🔌 Integration · ⚡ QWEDLocal · 🖥️ CLI · 🆓 Ollama (FREE!) · 📖 Full Documentation

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⚠️ What QWED Is (and Isn't)

QWED is: An open-source engineering tool that combines existing verification libraries (SymPy, Z3, SQLGlot, AST) into a unified API for LLM output validation.

QWED is NOT: Novel research. We don't claim algorithmic innovation. We claim practical integration for production use cases.

Works when: Developer provides ground truth (expected values, schemas, contracts) and LLM generates structured output.

Doesn't work when: Specs come from natural language, outputs are freeform text, or verification domain is unsupported.

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🚀 Quick Start: Install & Verify in 30 Seconds

# Install from PyPI (Recommended)
pip install qwed

# Or install from source
git clone https://github.com/QWED-AI/qwed-verification.git
cd qwed-verification
pip install -e .

from qwed_sdk import QWEDClient

client = QWEDClient(api_key="your_key")

# The LLM says: "Derivative of x^2 is 3x" (Hallucination!)
response = client.verify_math(
    query="What is the derivative of x^2?",
    llm_output="3x" 
)

print(response)
# -> ❌ CORRECTED: The derivative is 2x. (Verified by SymPy)

🆕 NEW: Client-Side Verification (v2.1.0)

No backend server? No problem! Use QWEDLocal:

from qwed_sdk import QWEDLocal

# FREE with Ollama (local LLMs)
client = QWEDLocal(
    base_url="http://localhost:11434/v1",
    model="llama3"
)

# Or use OpenAI
client = QWEDLocal(
    provider="openai",
    api_key="sk-...",
    model="gpt-4o-mini"
)

result = client.verify_math("What is 2+2?")
print(result.verified)  # True
print(result.value)  # 4

Features:

• ✅ Works with ANY LLM (Ollama, OpenAI, Anthropic, Gemini)
• ✅ $0 cost with local models
• ✅ Smart caching (50-80% cost savings)
• ✅ 100% private (data never leaves your machine)

🔒 NEW in v2.2.0: PII Masking (Enterprise Privacy)

Automatically mask sensitive data before sending to LLMs:

from qwed_sdk import QWEDLocal

# Enable PII masking (requires pip install 'qwed[pii]')
client = QWEDLocal(
    provider="openai",
    api_key="sk-...",
    mask_pii=True  # 🔒 Privacy protection
)

result = client.verify("My email is [e-mail]")
# LLM sees: "My email is <EMAIL_ADDRESS>"

# Check what was masked
print(result.evidence['pii_masked'])
# {'pii_detected': 1, 'types': ['EMAIL_ADDRESS']}

Compliance:

• 🏥 HIPAA (Healthcare)
• 🏦 PCI-DSS (Finance)
• 🇪🇺 GDPR (EU Privacy)

CLI:

qwed verify "Email: [e-mail]" --mask-pii
qwed pii "My SSN is 123-45-6789"  # Test detection

📖 PII Masking Guide → | QWEDLocal Guide → | CLI Reference →

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🚨 The LLM Hallucination Problem: Why AI Can't Be Trusted

Everyone is trying to fix AI hallucinations by Fine-Tuning (teaching it more data).

This is like forcing a student to memorize 1,000,000 math problems.

What happens when they see the 1,000,001st problem? They guess.

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📊 The Proof: Why Enterprise AI Needs QWED Verification

We benchmarked Claude Opus 4.5 (one of the world's best LLMs) on 215 critical tasks.

Finding	Implication
Finance: 73% accuracy	Banks can't use raw LLM for calculations
Adversarial: 85% accuracy	LLMs fall for authority bias tricks
QWED: 100% error detection	All 22 errors caught before production

QWED doesn't compete with LLMs. We ENABLE them for production use.

📄 Full Benchmark Report →

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🎯 Use Cases & Applications

QWED is designed for industries where AI errors have real consequences:

Industry	Use Case	Risk Without QWED
🏦 Financial Services	Transaction validation, fraud detection	$12,889 error per miscalculation
🏥 Healthcare AI	Drug interaction checking, diagnosis verification	Patient safety risks
⚖️ Legal Tech	Contract analysis, compliance checking	Regulatory violations
📚 Educational AI	AI tutoring, assessment systems	Misinformation to students
🏭 Manufacturing	Process control, quality assurance	Production defects

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✅ The Solution: Verification Layer

QWED is the first open-source Neurosymbolic AI Verification Layer.

We combine:

• Neural Networks (LLMs) for natural language understanding
• Symbolic Reasoning (SymPy, Z3, AST) for deterministic verification

The Core Philosophy: "The Untrusted Translator"

QWED operates on a strict principle: Don't trust the LLM to compute or judge; trust it only to translate.

Example Flow:

User Query: "If all A are B, and x is A, is x B?"

↓ (LLM translates)

Z3 DSL: Implies(A(x), B(x))

↓ (Z3 proves)

Result: TRUE (Proven by formal logic)

The LLM is an Untrusted Translator. The Symbolic Engine is the Trusted Verifier.

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💡 How QWED Compares

The "Judge" Problem

Most AI safety tools use "LLM-as-a-Judge" (asking GPT-4 to grade GPT-3.5). This is fundamentally unsafe:

• Recursive Hallucination: If the judge has the same bias as the generator, errors go undetected
• Probabilistic Evaluation: LLMs give probability, not proof
• Subjectivity: Different judges = different answers

QWED introduces "Solver-as-a-Judge": Replace neural network opinions with compiler execution and mathematical proof.

Comparison Table

Feature	QWED Protocol	NeMo Guardrails	LangChain Evaluators
The "Judge"	Deterministic Solver (Z3/SymPy)	Semantic Matcher	Another LLM (GPT-4)
Mechanism	Translation to DSL	Vector Similarity	Prompt Engineering
Verification Type	Mathematical Proof	Policy Adherence	Consensus/Opinion
Primary Goal	Correctness (Truth)	Safety (Appropriateness)	Quality Score
False Positives	Near Zero (Logic-based)	Medium (Semantic drift)	High (Subjectivity)
Works Offline	✅ Yes (QWEDLocal)	❌ No	❌ No
Privacy	✅ 100% Local	❌ Cloud-based	❌ Cloud-based

QWED's Advantage: When you need proof, not opinion.

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🔬 The Verification Engines

QWED routes queries to specialized engines that act as DSL interpreters:

┌──────────────┐
│  User Query  │
└──────┬───────┘
       │
       ▼
┌──────────────────┐
│ LLM (The Guesser)│
│ GPT-4 / Claude   │
└──────┬───────────┘
       │ Unverified Output
       ▼
┌────────────────────┐
│  QWED Protocol     │
│  (Verification)    │
└──────┬─────────────┘
       │
   ┌───┴────┐
   ▼        ▼
❌ Reject  ✅ Verified
            │
            ▼
   ┌────────────────┐
   │ Your Application│
   └────────────────┘

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QWED 🆚 Traditional AI Safety Approaches

Approach	Accuracy	Deterministic	Explainable	Best For
QWED Verification	✅ 99%+	✅ Yes	✅ Full trace	Production AI
Fine-tuning / RLHF	⚠️ ~85%	❌ No	❌ Black box	General improvement
RAG (Retrieval)	⚠️ ~80%	❌ No	⚠️ Limited	Knowledge grounding
Prompt Engineering	⚠️ ~70%	❌ No	⚠️ Limited	Quick fixes
Guardrails	⚠️ Variable	❌ No	⚠️ Reactive	Content filtering

QWED doesn't replace these - it complements them with mathematical certainty.

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🔧 The 8 Verification Engines: How QWED Validates LLM Outputs

We don't use another LLM to check your LLM. That's circular logic.

We use Hard Engineering:

Engine	Tech Stack	What it Solves
🧮 Math Verifier	SymPy + NumPy	Calculus, Linear Algebra, Finance. No more $1 + $1 = $3.
⚖️ Logic Verifier	Z3 Prover	Formal Verification. Checks for logical contradictions.
🛡️ Code Security	AST + Semgrep	Catches eval(), secrets, vulnerabilities before code runs.
📊 Stats Engine	Pandas + Wasm	Sandboxed execution for trusted data analysis.
🗄️ SQL Validator	SQLGlot	Prevents Injection & validates schema.
🔍 Fact Checker	TF-IDF + NLI	Checks grounding against source docs.
👁️ Image Verifier	OpenCV + Metadata	Verifies image dimensions, format, pixel data.
🤝 Consensus Engine	Multi-Provider	Cross-checks GPT-4 vs Claude vs Gemini.

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🧠 The QWED Philosophy: Verification Over Correction

❌ Wrong Approach	✅ QWED Approach
"Let's fine-tune the model to be more accurate"	"Let's verify the output with math"
"Trust the AI's confidence score"	"Trust the symbolic proof"
"Add more training data"	"Add a verification layer"
"Hope it doesn't hallucinate"	"Catch hallucinations deterministically"

QWED = Query with Evidence and Determinism

Probabilistic systems should not be trusted with deterministic tasks. If it can't be verified, it doesn't ship.

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🔌 LLM Framework Integrations

Already using an Agent framework? QWED drops right in.

🦜 LangChain (Native Integration)

Install: pip install 'qwed[langchain]'

from qwed_sdk.integrations.langchain import QWEDTool
from langchain.agents import initialize_agent
from langchain_openai import ChatOpenAI

# Initialize QWED verification tool
tool = QWEDTool(provider="openai", model="gpt-4o-mini")

# Add to your agent
llm = ChatOpenAI()
agent = initialize_agent(tools=[tool], llm=llm)

# Agent automatically uses QWED for verification
agent.run("Verify: what is the derivative of x^2?")

🤖 CrewAI

from qwed_sdk.crewai import QWEDVerifiedAgent

agent = QWEDVerifiedAgent(role="Analyst", allow_dangerous_code=False)

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🌍 Multi-Language SDK Support

Language	Package	Status
🐍 Python	qwed	✅ Available on PyPI
🟦 TypeScript	@qwed-ai/sdk	✅ Available on npm
🐹 Go	qwed-go	🟡 Coming Soon
🦀 Rust	qwed	🟡 Coming Soon

git clone https://github.com/QWED-AI/qwed-verification.git cd qwed-verification pip install -r requirements.txt

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## 🎯 Real Example: The $12,889 Bug

**User asks AI:** "Calculate compound interest: $100K at 5% for 10 years"

**GPT-4 responds:** "$150,000"  
*(Used simple interest by mistake)*

**With QWED:**
```python
response = client.verify_math(
    query="Compound interest: $100K, 5%, 10 years",
    llm_output="$150,000"
)
# -> ❌ INCORRECT: Expected $162,889.46
#    Error: Used simple interest formula instead of compound

Cost of not verifying: $12,889 error per transaction 💸

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❓ Frequently Asked Questions

Q: How does QWED differ from RAG (Retrieval Augmented Generation)?

A: RAG improves the input to the LLM by grounding it in documents. QWED verifies the output deterministically. RAG adds knowledge; QWED adds certainty.

Q: Can QWED work with any LLM?

A: Yes! QWED is model-agnostic and works with GPT-4, Claude, Gemini, Llama, Mistral, and any other LLM. We verify outputs, not models.

Q: Does QWED replace fine-tuning?

A: No. Fine-tuning makes models better at tasks. QWED verifies they got it right. Use both.

Q: Is QWED open source?

A: Yes! Apache 2.0 license. Enterprise features (audit logs, multi-tenancy) are in a separate repo.

Q: What's the latency overhead?

A: Typically <100ms for most verifications. Math and logic proofs are instant. Consensus checks take longer (multiple API calls).

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📚 Documentation & Resources

Resource	Description
📖 Full Documentation	Complete API reference and guides
🔧 API Reference	Endpoints and schemas
📊 Benchmarks	LLM accuracy testing results
🤝 Contributing Guide	How to contribute to QWED
🏗️ Architecture	System design and engine internals
🔒 Security Policy	Reporting vulnerabilities

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🏢 Enterprise Features

Need observability, multi-tenancy, audit logs, or compliance exports?

📧 Contact: [e-mail]

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📄 License

Apache 2.0 - See LICENSE

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⭐ Star History

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Current Stars:

View trend: Star History Page

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👥 Contributors

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📄 Citation

If you use QWED in your research or project, please cite our archived paper:

@software{dass2025qwed,
  author = {Dass, Rahul},
  title = {QWED Protocol: Deterministic Verification for Large Language Models},
  year = {2025},
  publisher = {Zenodo},
  version = {v1.0.0},
  doi = {10.5281/zenodo.18110785},
  url = {https://doi.org/10.5281/zenodo.18110785}
}

Plain text:

Dass, R. (2025). QWED Protocol: Deterministic Verification for Large Language Models (Version v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.18110785

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✅ Using QWED in Your Project?

Add this badge to your README to show you're using verified AI:

[![Verified by QWED](https://img.shields.io/badge/Verified_by-QWED-00C853?style=flat&logo=checkmarx)](https://github.com/QWED-AI/qwed-verification)

Preview:

This badge tells users that your LLM outputs are deterministically verified, not just "hallucination-prone guesses."

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⭐ Star us if you believe AI needs verification

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"Safe AI is the only AI that scales."

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