A Dual-Hypothesis Reasoning Framework for LLM Guardrails

2026-07-20

AI Summary

Current safety guardrails for large language models lack an explicit trade-off between safe and unsafe interpretations when assessing user inputs, leading to unreliable and poorly explainable decisions. This work proposes ARBITER, a novel framework that introduces dual-hypothesis reasoning to simultaneously model both safe and unsafe interpretations of a prompt. By integrating multi-component supervised fine-tuning (MC-SFT), parameter-efficient LoRA adaptation, and self-generated reasoning traces, ARBITER achieves high performance at low cost. The method consistently outperforms existing reasoning-based and non-reasoning guardrails across three safety evaluation benchmarks, demonstrating particularly strong generalization in out-of-domain settings. Moreover, it produces faithful and interpretable evidence phrases that support its safety judgments.

Abstract

We propose ARBITER, a novel LLM guardrail framework that introduces two key ideas: (i) dual-hypothesis reasoning, a reasoning method for LLM guardrails that explicitly considers both safe and unsafe interpretations of a prompt before making a safety decision, and (ii) multi-component supervised fine-tuning (MC-SFT), a structured training loss for reasoning-based guardrails that decomposes LLM outputs into logical components and weights them according to their importance. Existing reasoning-based guardrails often rely on expensive procedures, such as generating reasoning traces using larger or closed-source teacher models and applying full-parameter fine-tuning. In contrast, ARBITER uses a cost-effective self-generation strategy for reasoning traces and LoRA-based parameter-efficient fine-tuning while still achieving better performance than these expensive approaches. Additionally, ARBITER provides faithful evidence-phrase explanations for unsafe decisions, enabling a more transparent and interpretable guardrail method. Experiments on three safety moderation benchmarks show that ARBITER outperforms existing reasoning-based and non-reasoning guardrail baselines, with clear gains in out-of-domain evaluations.

Problem

• LLM guardrails
• safety moderation
• reasoning-based safety
• interpretability
• out-of-domain generalization

Innovation

• dual-hypothesis reasoning
• multi-component supervised fine-tuning
• parameter-efficient fine-tuning
• reasoning-based guardrails
• interpretable safety moderation