DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail

• URL: http://arxiv.org/abs/2607.06326v1
• Date: Tue, 07 Jul 2026 14:25:05 GMT
• Title: DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail
• Abstract summary: We present DT-Guard, a content safety guardrail model based on a Reasoning-Active Training, Reasoning-Free Inference paradigm.<n>The key idea is to use reasoning supervision during training while emitting only structured safety labels at inference time.
• Score: 19.59614205209542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models deployed in open-world applications require safety guardrails that are both robust to complex risks and efficient enough for low-latency runtime moderation. Existing guardrails face a practical trade-off between lightweight classification-based models, which are efficient but often struggle with concealed intent, ambiguous semantics, and borderline safety decisions, and reasoning-based guards, which improve judgment quality but introduce additional token generation and inference latency. We present DT-Guard, a content safety guardrail model based on a Reasoning-Active Training, Reasoning-Free Inference paradigm. The key idea is to use reasoning supervision during training while emitting only structured safety labels at inference time. DT-Guard formulates safety judgment as a progressive decision process, Intent - Category - Safety, and constructs an intent-driven dataset with intent labels, risk categories, safety labels, and structured reasoning trajectories. To further improve hard-case robustness, we propose Rollout-Guided Progressive Hard-Case Optimization (RG-PHO), which uses multi-rollout consistency to identify stably mastered, persistently failed, and preference-unstable samples, and applies targeted supervised and preference optimization accordingly. At inference time, DT-Guard directly generates structured labels without explicit reasoning traces, preserving deployment efficiency. Experiments on prompt-side and response-side safety benchmarks show that DT-Guard achieves average F1 scores of 0.886 and 0.870, respectively. With only a 4B backbone, it reaches a dual-side average F1 of 0.878, outperforming strong 8B guardrail baselines. These results demonstrate that reasoning supervision can be effectively internalized into low-latency safety discrimination.

Related papers

• Paved with True Intents: Intent-Aware Training Improves LLM Safety Classification Across Training Regimes [5.493750878637108]
  We introduce AIMS, a human-annotated dataset of 1,724 difficult safety prompts.<n>We use AIMS to evaluate intent-aware training across supervised fine-tuning, preference learning, reasoning distillation, and reinforcement learning.
  arXiv  Detail & Related papers  (2026-06-25T16:03:57Z)
• ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails [57.25411733152009]
  Reasoning-based guardrails improve safety moderation by generating explicit rationales before issuing final decisions.<n>A model may recognize a harmful intent in its reasoning but still predict a safe label, or issue an unsafe decision without policy-grounded justification.<n>We propose ConsisGuard, a consistency-aware framework for reasoning-based guardrails.
  arXiv  Detail & Related papers  (2026-05-29T09:42:08Z)
• Robust and Efficient Guardrails with Latent Reasoning [23.837448508316513]
  Reasoning-based guardrails incur substantial query latency and token overhead.<n>We propose COLAGUARD, a guardrail model that transfers multi-step safety reasoning into a continuous latent space.<n>Our results suggest latent reasoning offers a practical alternative to explicit rationale generation for deployable guardrails.
  arXiv  Detail & Related papers  (2026-05-27T20:15:22Z)
• TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories [20.868825285848196]
  Safety guardrails are well-benchmarked for natural language responses, but their efficacy remains largely unexplored within multi-step tool-use trajectories.<n>To address this gap, we introduce StructuralSafe-Bench, the first comprehensive benchmark specifically designed to assess mid-trajectory safety.
  arXiv  Detail & Related papers  (2026-04-08T15:46:14Z)
• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• DRAFT: Task Decoupled Latent Reasoning for Agent Safety [59.46137757545185]
  We propose DRAFT (Task Decoupled Latent Reasoning for Agent Safety), a latent reasoning framework that decouples safety judgment into two trainable stages.<n>An Extractor distills the full trajectory into a compact continuous latent draft, and a Reasoner that jointly attends to the draft and the original trajectory to predict safety.<n>DRAFT consistently outperforms strong baselines, improving accuracy from 63.27% (LoRA) to 91.18% averaged over benchmarks.
  arXiv  Detail & Related papers  (2026-02-11T07:45:14Z)
• Steering Externalities: Benign Activation Steering Unintentionally Increases Jailbreak Risk for Large Language Models [62.16655896700062]
  Activation steering is a technique to enhance the utility of Large Language Models (LLMs)<n>We show that it unintentionally introduces critical and under-explored safety risks.<n>Experiments reveal that these interventions act as a force multiplier, creating new vulnerabilities to jailbreaks and increasing attack success rates to over 80% on standard benchmarks.
  arXiv  Detail & Related papers  (2026-02-03T12:32:35Z)
• Self-Guard: Defending Large Reasoning Models via enhanced self-reflection [54.775612141528164]
  Self-Guard is a lightweight safety defense framework for Large Reasoning Models.<n>It bridges the awareness-compliance gap, achieving robust safety performance without compromising model utility.<n>Self-Guard exhibits strong generalization across diverse unseen risks and varying model scales.
  arXiv  Detail & Related papers  (2026-01-31T13:06:11Z)
• ProGuard: Towards Proactive Multimodal Safeguard [48.89789547707647]
  ProGuard is a vision-language proactive guard that identifies and describes out-of-distribution (OOD) safety risks.<n>We first construct a modality-balanced dataset of 87K samples, each annotated with both binary safety labels and risk categories.<n>We then train our vision-language base model purely through reinforcement learning to achieve efficient and concise reasoning.
  arXiv  Detail & Related papers  (2025-12-29T16:13:23Z)
• ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails [33.96886111900147]
  ThinkGuard is a critique-augmented guardrail model that distills knowledge from high-capacity language models.<n>It achieves the highest average F1 and AUPRC, outperforming all baselines.<n>It surpasses label-only fine-tuned models, confirming that structured critiques enhance both classification precision and nuanced safety reasoning.
  arXiv  Detail & Related papers  (2025-02-19T06:09:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.