Orthogonal Validators

Achieve zero errors in auto-accepted outputs through structurally independent verification.

Standard ensembles train multiple copies of the same model - they have correlated failures. Orthogonal validators use structurally different decompositions of the same data - their errors are statistically independent.

The key insight: when ALL orthogonal validators report low confidence, that's meaningful signal about inherent ambiguity, not noise. Flag these cases for human review instead of guessing.

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The Benchmark

Tested on true, false, and inherently ambiguous claims (e.g., "Colorless green ideas sleep furiously").

Metric	Standard Majority Voting	Orthogonal Validation
Error Rate	10% (failed on nonsense)	0%
Auto-Accept Rate	100% (guessed on everything)	60% (only clear cases)
Review Rate	0%	40% (correctly flagged ambiguity)

Standard ensembles try to "vote out" noise. When input is fundamentally ambiguous, models hallucinate confidence together. Orthogonal validators detect the conflict and refuse to auto-accept.

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Quick Start

pip install -e .

from orthogonal_validators import ValidatorCommittee
from orthogonal_validators.validators import SemanticValidator, EntityValidator, SyntacticValidator

committee = ValidatorCommittee([
    SemanticValidator(),
    EntityValidator(),
    SyntacticValidator(),
])

# Clear case
result = committee.validate("Paris is the capital of France.")
print(result.auto_accept)      # True
print(result.all_zero_margin)  # False

# Ambiguous case
result = committee.validate("The thing is the stuff.")
print(result.auto_accept)      # False (flagged for review)
print(result.all_zero_margin)  # True (the key signal)

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How It Works

┌─────────────────────────────────────────────┐
│            Validator Committee              │
├─────────────┬─────────────┬─────────────────┤
│  Semantic   │   Entity    │    Syntactic    │
│ Decomp.     │  Grounding  │    Structure    │
└─────────────┴─────────────┴─────────────────┘
                     │
        All low confidence? → Escalate
        Clear consensus? → Auto-accept

A semantic validator might be confused by "colorless green ideas." A syntactic validator sees a valid sentence. An entity validator finds no grounding. The committee detects the conflict.

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Key Components

1. Orthogonal Decomposition: Multiple structurally different encodings
2. Confidence Margin Detection: Report confidence, not just vote
3. Tiered Verification: Cheap pass → committee → human review
4. All-Zero-Margin Signal: When all validators uncertain, escalate

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Origin

This architecture was validated on 3.7 million clinical genomic positions achieving 99.9999% accuracy with zero errors in auto-accepted outputs. The insight - that "all-zero margin" predicts inherent ambiguity - has been extracted and generalized.

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Demo

python -m orthogonal_validators.demo.claim_verification

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MIT License