arXiv AI October 15, 2026 By Zhinan Liu, Jie Li, Mingyu Kang, Jiayi Ji

$\mathrm{R}{\scriptstyle\mathrm{EAL}}\mathrm{G}{\scriptstyle\mathrm{UARD}}$: Reliable, Efficient, and Auditable LLM Safeguards via Latent Reasoning

arXiv:2608. 03838v2 Announce Type: replace Abstract: Reasoning-based large language model (LLM) safeguards improve safety classification accuracy by generating explicit rationales before making decisions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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 26

Do Safety Guardrails Need to Reason? LeanGuard: A Fast and Light Approach for Robust Moderation

arXiv:2606. 26686v1 Announce Type: new Abstract: In order to screen a prompt or a response, the recent guardrail methods generate a chain-of-thought (CoT) before they issue a verdict.

By Dongbin Na

robotics benchmarks safety

arXiv Machine Learning

Jun 25

Do Thinking Tokens Help with Safety?

arXiv:2606. 25013v1 Announce Type: new Abstract: Today's reasoning models use thinking tokens to attain stronger performance on benchmarks than their instruction-tuned counterparts.

By Narutatsu Ri, Abhishek Panigrahi, Sanjeev Arora

llms benchmarks safety

arXiv AI

Sep 1

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning

arXiv:2605.11467v2 Announce Type: replace-cross Abstract: Reasoning models post-hoc rationalize answers they have already committed to internally, producing chains of *reasoning theater*: deliberativ...

By Swapnil Parekh, Naman Goyal

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 9

Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human

arXiv:2606. 08919v1 Announce Type: new Abstract: As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approval gate: risky actions pause and wait for a person.

By Emre Turan

llms agents safety

arXiv AI

Jul 10

Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets

arXiv:2607. 08173v1 Announce Type: new Abstract: Black box auditing of language models is an essential pre-deployment tool, but it may miss subtle forms of misalignment and hidden information.

By Jack Hopkins, Dipika Khullar, Fabien Roger

llms

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Related stories

LatentGuard: Efficient and Inspectable Latent Reasoning for LLM Safeguards

Do Safety Guardrails Need to Reason? LeanGuard: A Fast and Light Approach for Robust Moderation

Do Thinking Tokens Help with Safety?

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning

Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human

Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets