Large Language Models (LLMs) can generate content spanning ideological rhetoric to explicit instructions for violence. However, existing safety evaluations often rely on simplistic binary labels (safe/unsafe), overlooking the nuanced spectrum of risk these outputs pose. To address this, we present XGUARD, a benchmark and evaluation framework designed to assess the severity of extremist content generated by LLMs on a multi-level grading. It includes 3,840 red-teaming prompts generated using templates informed by real-world extremist scenarios from social media, forums, and news. The framework categorizes model responses into five danger levels (0–4) defined by degree of extremist endorsement, enabling nuanced analysis of failure frequency and severity. We introduce the interpretable Attack Severity Curve (ASC) to visualize vulnerabilities and compare defense mechanisms across threat intensities. Using XGUARD, we evaluate five popular LLMs and two lightweight defense strategies, revealing key insights into current safety gaps and trade-offs between robustness and expressive freedom. Our work underscores the value of graded safety metrics for building trustworthy LLMs. The code and dataset is available at https://github.com/Abishethvarman/XGUARD

We introduce MedBench, a benchmark for evaluating medical language models as deliberating agents rather than isolated predictors. MedBench evaluates eight models (4B?32B) on 19,625 questions from six medical QA datasets using Consensus-Aware Model Panel (CAMP), a two-tier protocol in which five 4B?8B models answer independently, revise after observing peer reasoning, and escalate persistent disagreements to larger 20B?32B models. Compared with zero-shot, few-shot, and chain-of-thought baselines, CAMP shows that deliberation is not uniformly accuracy-improving, but reveals interaction-driven behaviors hidden by single-model evaluation. On PubMedQA without external context, the 4B?8B panel outperforms the evaluated 20B?32B individual zero-shot models (54.1% vs. 33.9%), and achieves the best evaluated result with context (75.7%), suggesting that structured interaction can sometimes complement scale. Across five datasets, initial inter-model agreement is positively associated with correctness and serves as a useful difficulty signal. However, on MedXpertQA, unanimous agreement yields only 6.6% accuracy despite 14.4% overall accuracy, suggesting correlated ignorance, where shared biases make consensus misleading. Error analysis shows that most failures are debate-insufficient cases, where incorrect majorities persist despite interaction (93?97%), while debate-harmful cases account for 3?7%. MedBench positions deliberative evaluation as a complement to accuracy-centric benchmarking, measuring when model interaction corrects errors, reinforces shared mistakes, or signals the need for stronger evidence and human review.