STEER: Inference-Time Risk Control via Constrained Quality-Diversity Search

TL;DR

STEER is a training-free framework that enhances large language models by enabling tunable risk control through a constrained quality-diversity search, improving safety and coverage in clinical decision tasks.

Contribution

STEER introduces a novel, inference-time risk control method that constructs a diverse persona population for LLMs, allowing adjustable decision conservativeness without retraining.

Findings

Broader behavioral coverage than temperature sampling

Higher accuracy on urgent cases compared to post-training methods

Effective risk control without domain performance loss

Abstract

Large Language Models (LLMs) trained for average correctness often exhibit mode collapse, producing narrow decision behaviors on tasks where multiple responses may be reasonable. This limitation is particularly problematic in ordinal decision settings such as clinical triage, where standard alignment removes the ability to trade off specificity and sensitivity (the ROC operating point) based on contextual constraints. We propose STEER (Steerable Tuning via Evolutionary Ensemble Refinement), a training-free framework that reintroduces this tunable control. STEER constructs a population of natural-language personas through an offline, constrained quality-diversity search that promotes behavioral coverage while enforcing minimum safety, reasoning, and stability thresholds. At inference time, STEER exposes a single, interpretable control parameter that maps a user-specified risk percentile to a selected persona, yielding a monotonic adjustment of decision conservativeness. On two clinical triage benchmarks, STEER achieves broader behavioral coverage compared to temperature-based sampling and static persona ensembles. Compared to a representative post-training method, STEER maintains substantially higher accuracy on unambiguous urgent cases while providing comparable control over ambiguous decisions. These results demonstrate STEER as a safety-preserving paradigm for risk control, capable of steering behavior without compromising domain competence.