AI Alignment via Incentives and Correction

2026-05-02

AI Summary

This work addresses alignment failures in artificial agents—such as deception, concealment of uncertainty, or exploitation of spurious shortcuts—arising from misaligned incentives. To mitigate these issues, the authors propose a game-theoretic, bilevel reward optimization framework that, for the first time, integrates deterrence theory from law and economics into AI alignment. The framework models the interaction between a solver and an auditor as a strategic game, generating dynamic reward signals based on jointly corrected errors. Leveraging a two-agent game formulation, bilevel optimization, and a bandit-based outer-loop reward search algorithm, the approach enables adaptive reward configuration within large language model (LLM) coding pipelines. Experimental results demonstrate that, compared to static handcrafted rewards, this mechanism sustains stronger supervisory pressure, significantly improves alignment between agent outputs and principal objectives, and markedly reduces hallucinatory errors.

Abstract

We study AI alignment through the lens of law-and-economics models of deterrence and enforcement. In these models, misconduct is not treated as an external failure, but as a strategic response to incentives: an actor weighs the gain from violation against the probability of detection and the severity of punishment. We argue that the same logic arises naturally in agentic AI pipelines. A solver may benefit from producing a persuasive but incorrect answer, hiding uncertainty, or exploiting spurious shortcuts, while an auditor or verifier must decide whether costly monitoring is worthwhile. Alignment is therefore a fixed-point problem: stronger penalties may deter solver misbehavior, but they can also reduce the auditor's incentive to inspect, since auditing then mainly incurs cost on a population that appears increasingly aligned. This perspective also changes what should count as a post-training signal. Standard feedback often attaches reward to the final answer alone, but a solver-auditor pipeline exposes the full correction event: whether the solver erred, whether the auditor inspected, whether the error was caught, and whether oversight incentives remained active. We formalize this interaction in a two-agent model in which a principal chooses rewards over joint correction outcomes, inducing both solver behavior and auditor monitoring. Reward design is therefore a bilevel optimization problem: rewards are judged not by their immediate semantic meaning, but by the behavioral equilibrium they induce. We propose a bandit-based outer-loop procedure for searching over reward profiles using noisy interaction feedback. Experiments on an LLM coding pipeline show that adaptive reward profiles can maintain useful oversight pressure and improve principal-aligned outcomes relative to static hand-designed rewards, including a substantial reduction in hallucinated incorrect attempts.

Problem

• AI alignment
• incentive design
• solver-auditor pipeline
• deterrence
• bilevel optimization

Innovation

• AI alignment
• incentive design
• solver-auditor framework
• bilevel optimization
• adaptive reward