DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail
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 Tra
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 54%wbopan/flashtrace →
- PossiblePossibly related (embedding) · 48%sileod/reasoning-core →
- PossiblePossibly related (embedding) · 48%benjaminzwhite/reasoning-models →
- PossiblePossibly related (embedding) · 47%IEEE Rolls Out Large Language Models Virtual Training Course →
- PossiblePossibly related (embedding) · 29%guardrails-ai/guardrails →
“Possibly related via embedding similarity 0.58 (not asserted). Timestamp check: artifact after paper (+9d).”
- LinkedLinked via arxiv author · 85%Che Liu →
“DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail”
- LinkedLinked via arxiv author · 85%Changtao Miao →
“DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail”
- LinkedLinked via arxiv author · 85%Xinjie Yang →
“DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail”