arXiv AI July 21, 2026 By Md Asiful Islam, Mihai Surdeanu

A Dual-Hypothesis Reasoning Framework for LLM Guardrails

arXiv:2607. 17575v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 8

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

arXiv:2607. 06326v1 Announce Type: new 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.

By He Liu, Changtao Miao, Xinjie Yang, Tianle Song, Yin Wu, Junchi Chen, Bintao He, Xinyuan Zhang, Bo Zhang, Shi Yan, Wei Lu, Wei Wang, Danyang Xu, Jiansheng Cai, Zhe Li

llms efficiency benchmarks safety

arXiv AI

1d ago

PL-Guard: Probabilistic Logic Reasoning for LLM Guardrails

arXiv:2608. 15673v1 Announce Type: cross Abstract: Large language model guardrails can be viewed as policy-consistency problems: a system must determine which policy-relevant facts hold in a prompt-response pair and what those facts imply under a given policy.

By Satchit Chatterji, Shihan Wang, Giovanni Sileno, Erman Acar

llms benchmarks safety

arXiv AI

Jun 6

From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents

arXiv:2606. 05805v1 Announce Type: new Abstract: LLM-based guardrails typically safeguard agents by evaluating proposed actions or inputs before execution, producing safety signals such as binary allow/deny decisions, risk categories, and/or explanatory rationales about potential policy violations.

By Yuhao Sun, Jiacheng Zhang, Shaanan Cohney, Zhexin Zhang, Feng Liu, Xingliang Yuan

llms agents safety

arXiv AI

Aug 5

LatentGuard: Efficient and Inspectable Latent Reasoning for LLM Safeguards

arXiv:2608. 03838v1 Announce Type: new Abstract: Reasoning-based guard models improve LLM safeguards, but decoding explicit rationales for every interaction makes them costly to deploy.

By Zhinan Liu, Jie Li, Mingyu Kang, Jiayi Ji

llms safety

arXiv AI

Jun 10

SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning

arXiv:2604. 01993v2 Announce Type: replace-cross Abstract: Multi-hop QA benchmarks often reward Large Language Models (LLMs) for spurious correctness, where models reach correct answers through invalid intermediate reasoning.

By Daeyong Kwon, Soyoung Yoon, Seung-won Hwang

llms benchmarks

arXiv AI

Aug 5

Evading Chain-of-Thought Monitoring Through Model Poisoning

arXiv:2608. 02820v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning trace is informative about its actions.

By Giorgio Severi, Shujaat Mirza, Blake Bullwinkel, Amanda Minnich

fine-tuning safety

Related stories

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

PL-Guard: Probabilistic Logic Reasoning for LLM Guardrails

From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents

LatentGuard: Efficient and Inspectable Latent Reasoning for LLM Safeguards

SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning

Evading Chain-of-Thought Monitoring Through Model Poisoning