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
10 Pith papers cite this work. Polarity classification is still indexing.
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LPG compresses policy deliberation into 10 latent tokens to reach 84.5% safety accuracy and 11x speedup over explicit reasoning baselines on guardrail benchmarks.
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.
FRPO applies a max-min robust optimization over KL-bounded policy neighborhoods during RLHF to reduce catastrophic forgetting of safety and accuracy under subsequent SFT or RL fine-tuning.
Disentangled Safety Adapters decouple safety computations from task-optimized LLMs via lightweight adapters, yielding up to 53% better AUC on safety tasks and dynamic inference-time alignment with reduced performance trade-offs.
Introduces StanceNakba 2026 shared task and 2,606-post dataset for Pro-Palestine/Pro-Israel/Neutral stance in English posts and Favor/Against/Neither stance in Arabic posts on normalization and refugees, with top Macro F1 scores of 0.9620 and 0.8724 from transformer models.
Opir introduces efficient multi-task encoder models trained on a 996-category safety taxonomy that match or exceed larger baselines on most safety benchmarks while using under 100M parameters for edge variants.
citing papers explorer
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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.
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LPG: Balancing Efficiency and Policy Reasoning in Latent Policy Guardrails
LPG compresses policy deliberation into 10 latent tokens to reach 84.5% safety accuracy and 11x speedup over explicit reasoning baselines on guardrail benchmarks.
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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.
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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.
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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.
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Robust Policy Optimization to Prevent Catastrophic Forgetting
FRPO applies a max-min robust optimization over KL-bounded policy neighborhoods during RLHF to reduce catastrophic forgetting of safety and accuracy under subsequent SFT or RL fine-tuning.
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Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignment
Disentangled Safety Adapters decouple safety computations from task-optimized LLMs via lightweight adapters, yielding up to 53% better AUC on safety tasks and dynamic inference-time alignment with reduced performance trade-offs.
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StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse
Introduces StanceNakba 2026 shared task and 2,606-post dataset for Pro-Palestine/Pro-Israel/Neutral stance in English posts and Favor/Against/Neither stance in Arabic posts on normalization and refugees, with top Macro F1 scores of 0.9620 and 0.8724 from transformer models.
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Opir: Efficient Multi-Task Safety Classification for Toxicity, Jailbreaks, Hate Speech, and Harmful Content
Opir introduces efficient multi-task encoder models trained on a 996-category safety taxonomy that match or exceed larger baselines on most safety benchmarks while using under 100M parameters for edge variants.
- Safety Is Not Universal: The Selective Safety Trap in LLM Alignment