AI Reliability Framework
The Reliability Layer
for AI Systems
AI systems fail when they can't show what they compressed. CAI measures compression strain across semantically equivalent prompts, surfacing contradictions before they reach production.
Detect
Contradiction Density
Measure how often a model's outputs contradict each other across rephrased but semantically identical prompts.
Measure
Compression Strain
Score the tension at each compression site (retrieval, summarization, generation) before claims reach users.
Gate
Abstention & Provenance
Block unsupported claims at the source. Attach provenance to every output so failures are auditable.
What is CAI?
Every useful system compresses. Language models compress training data into weights. Retrievers compress corpora into ranked passages. Summarizers compress documents into paragraphs. When that compression is invisible, errors hide inside it and hallucinations are just compressed contradictions that the system never had to resolve.
Compression-Aware Intelligence makes the compression explicit. It defines a measurable signal called compression strain and provides a scoring framework that predicts where outputs will drift, routes around failure, and lets you ship safer AI systems.
The Core Metrics
Three composable signals that give you a single CAI reliability score.
Contradiction Density
$$CD = \frac{\text{contradictions detected}}{\text{total outputs evaluated}}$$
Coherence Delta
$$CoD = \text{Coherence}_{\text{before}} - \text{Coherence}_{\text{after}}$$
Entropy Risk Score
$$ERS = -\sum_i p_i \log(p_i)$$
Composite CAI Score
$$\text{CAI} = \alpha \cdot CD + \beta \cdot CoD + \gamma \cdot ERS$$
α, β, γ are tunable weights. Start with equal weighting and calibrate against your task accuracy baseline.
Explore the Framework
Start here
Proof
Falsifiable claims, experiments you can run today, and a counterexample challenge.
Data
Dataset
Prompt pairs, contradiction labels, and compression strain scores for benchmarking.
Build
Implementations
Ready-to-use patterns for adding CAI gating to real pipelines.
Apply
Applications
How CAI applies across medical, legal, financial, and software contexts.