Answer Engineering: Local Trajectory Editing for Protocol-Constrained Decision Making in Large Language Models

2026-06-19

AI Summary

Large language models often produce confident yet non-compliant responses in highly protocolized settings such as clinical decision-making. This work proposes Answer Engineering, a method that enables deterministic intervention during standard autoregressive generation by performing rule-based, local edits to the reasoning trajectory at runtime—without requiring model retraining or weight modifications. It is the first approach to support localized trajectory control without global search, significantly enhancing adherence to clinical protocols while preserving auditability. Evaluated on a benchmark for sudden sensorineural hearing loss, the method increases protocol compliance from 25.1% to 83.5% and improves balanced accuracy from 42.0% to 80.7%.

Abstract

Large language models can produce confident but protocol-invalid answers in domains where procedural compliance is critical. This paper presents Answer Engineering, a deterministic runtime and authoring layer that applies localized rule-guided interventions to the visible reasoning trajectory during standard autoregressive generation, without retraining, modifying model weights, or performing global search. The method is evaluated on a controlled clinical benchmark for sudden sensorineural hearing loss (SSNHL), where correct management depends on protocol-consistent interpretation of symptom timing, Weber/Rinne tuning-fork findings, and otoscopic findings. In the benchmark, step-by-step reasoning shifted rather than eliminated errors: compliant outcomes for SSNHL decreased from 54.5% under unguided generation to 25.1%, while acceptance on the conductive contrast condition increased from 1.6% to 58.9%. Local trajectory editing increased SSNHL compliance to 83.5% and conductive-case adherence to 77.9%, raising balanced accuracy from 42.0% under reasoning-only generation to 80.7%. The results support a systems-level view in which protocol adherence can be improved through auditable runtime control of reasoning trajectories, while also identifying limitations caused by rule coverage, trigger reliability, and persistent diagnosis-first generation dynamics.

Problem

• protocol-constrained decision making
• large language models
• reasoning trajectory
• compliance
• clinical reasoning

Innovation

• Answer Engineering
• local trajectory editing
• protocol-constrained reasoning
• runtime intervention
• large language models