Adaptive multi-agent LLM pipelines with bandit-based sampling achieve lower false positive rates (0.095 vs 0.159) than single-agent models on two behavioral health datasets while maintaining similar false negative rates.
AEGIS2.0: A diverse ai safety dataset and risks taxonomy for alignment of llm guardrails
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
GLiGuard is a compact schema-conditioned bidirectional encoder that matches 7B-27B guard models on safety benchmarks while delivering up to 16x higher throughput and 17x lower latency.
Annotator Policy Models learn safety policies from labeling behavior alone, accurately predicting responses and revealing sources of disagreement like policy ambiguity and value pluralism.
SIREN identifies safety neurons via linear probing on internal LLM layers and combines them with adaptive weighting to detect harm, outperforming prior guard models with 250x fewer parameters.
citing papers explorer
-
Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems
Adaptive multi-agent LLM pipelines with bandit-based sampling achieve lower false positive rates (0.095 vs 0.159) than single-agent models on two behavioral health datasets while maintaining similar false negative rates.
-
GLiGuard: Schema-Conditioned Classification for LLM Safeguard
GLiGuard is a compact schema-conditioned bidirectional encoder that matches 7B-27B guard models on safety benchmarks while delivering up to 16x higher throughput and 17x lower latency.
-
Understanding Annotator Safety Policy with Interpretability
Annotator Policy Models learn safety policies from labeling behavior alone, accurately predicting responses and revealing sources of disagreement like policy ambiguity and value pluralism.
-
LLM Safety From Within: Detecting Harmful Content with Internal Representations
SIREN identifies safety neurons via linear probing on internal LLM layers and combines them with adaptive weighting to detect harm, outperforming prior guard models with 250x fewer parameters.