arXiv:2608. 13345v1 Announce Type: new Abstract: Artificial Intelligence (AI) safety systems combine character shaping (e.
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arXiv:2410. 22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards.
arXiv:2607. 16112v1 Announce Type: new Abstract: Frontier AI companies have published capability thresholds that differ substantially, making it difficult for third parties to verify whether a threshold has been crossed or to compare requirements across companies.
The paper introduces FUSE, a modular framework that evaluates large language models (LLMs) for dangerous capabilities across three orthogonal pipelines: Knowledge (K), Defense (D), and Harm (H). Using a chemical‑biological module, the authors assess 12 commercial LLMs, revealing divergent profiles among models and families, and showing that newer models increase knowledge while only partially improving defense. The framework’s reliability is supported by high cross‑judge consistency and low inter‑pipeline correlations.
ReSI is a recursive safety improvement framework that iteratively applies diverse red‑teaming methods to identify vulnerabilities in AI models, develops training recipes, and selects the update with the greatest safety gain while retaining capability. Across four dense and mixture‑of‑experts models, ReSI matches or surpasses frontier models on both in‑distribution and out‑of‑distribution safety benchmarks, and outperforms alignment baselines on nearly all safety evaluations. The framework notably reduces the mean X‑Teaming attack success rate from 86.01% to 31.45%, outperforming GPT‑5.6‑Luna’s 56.69% and demonstrating stronger resilience to unseen attacks.
arXiv:2609.38411v1 Announce Type: new Abstract: Leading AI developers have reported agents acting beyond their approved limits, which a United Nations panel described as an early warning of loss of h...
The paper proves that using a permissive safety filter in reinforcement learning does not compromise asymptotic performance. By formalizing safety through a safety‑critical Markov decision process and a filtered MDP, the authors show that optimal policies in the filtered MDP achieve the same return as the best safe policy in the original setting. Experiments on Safety Gymnasium confirm zero violations during training and performance that matches or exceeds unfiltered baselines.