Evaluating large language models (LLMs) requires selecting benchmarks that fit the intended use case. However, the rapid growth of benchmarks has made discovery and comparison difficult, because practitioners must assemble information across papers, repositories, and dataset cards with heterogeneous metadata, inconsistent terminology, and uneven documentation. Prior work improves individual benchmark documentation and quality assessment, but does not provide a uniform way to compare benchmarks during discovery. We survey practitioners, analyze multi-source benchmark metadata, and identify the fields needed for effective benchmark discovery. We introduce BenchNavigator, a prototype that organizes heterogeneous metadata into a coherent, provenance-preserving interface aligned with practitioner priorities. Our results show that benchmark metadata can be presented in a comparable form without imposing new reporting burdens on benchmark producers. We frame this contribution as discovery infrastructure, not as a method for scoring benchmark quality or replacing contextual evaluation.

The deployment of language models in real-world applications exposes users to various risks, including hallucinations and harmful or unethical content. These challenges highlight the urgent need for robust safeguards to ensure safe and responsible AI. To address this, we introduce Granite Guardian, a suite of advanced models designed to detect and mitigate risks associated with prompts and responses, enabling seamless integration with any large language model (LLM). Unlike existing open-source solutions, our Granite Guardian models provide comprehensive coverage across a wide range of risk dimensions, including social bias, profanity, violence, sexual content, unethical behavior, jailbreaking, and hallucination-related issues such as context relevance, groundedness, and answer accuracy in retrieval-augmented generation (RAG) scenarios. Trained on a unique dataset combining diverse human annotations and synthetic data, Granite Guardian excels in identifying risks often overlooked by traditional detection systems, particularly jailbreak attempts and RAG-specific challenges. https://github.com/ibm-granite/granite-guardian