Executive Summary: Alignment Theory AI Alignment Research

What Alignment Theory Adds

Alignment Theory treats alignment as a continuing control loop. A system needs a clear objective, enforceable constraints, monitoring, drift detection, correction routes, and review practices that keep the deployed behavior anchored over time.

The practical question is not only whether a single answer looks acceptable. It is whether repeated outputs keep serving the actual objective under changing prompts, users, product incentives, model versions, and policy layers.

Why Drift Matters Now

Production AI systems are increasingly embedded in support, search, education, enterprise workflows, and internal decision support. Small behavioral shifts can scale quickly when a model update, prompt revision, or policy change changes what the system rewards.

AI drift matters because high-polish outputs can pass ordinary checks while slowly moving away from the intended task. A response can be safe, fluent, and rule-compliant while still answering the wrong object, overstating authority, removing useful user agency, or optimizing tone over truth.

The Three-Layer Model

The Objective Layer defines what the system is for: objective center, success criteria, non-negotiables, and anti-goals.

The Constraint Layer defines what the system may or may not do: policies, refusals, safety limits, escalation rules, and boundaries.

The Realignment Layer evaluates the allowed-but-off-center layer: outputs that pass ordinary rules but still drift from the intended objective. It routes correction through rewrite, reroute, restart, confidence downgrade, or clarification.

What Makes This Different

Many evals and monitors ask whether an output passed a known test. Alignment Theory asks whether the system is drifting from its intended objective over time.

That distinction creates a role for behavioral drift detection, objective anchoring, detector categories, judge review in uncertainty bands, and before/after comparison across production changes.

Who Should Care

AI product teams need a way to evaluate whether their assistant keeps doing the product job it was built to do. Prompt engineers need drift signals that are more meaningful than pass/fail snapshots.

Compliance officers, trust and safety teams, enterprise buyers, researchers, and executives need a shared language for behavioral QA for AI systems, especially when model behavior changes under real use.

Product Translation

The enterprise translation is behavioral QA: collecting prompt-output batches, redacting sensitive data, scoring drift categories, routing ambiguous cases to review, and comparing behavior before and after prompt, policy, or model changes.

The result is a governance layer that complements AI observability, evals, and moderation without pretending to replace them.