Commonsense reasoning is a key language model capability, as it is purportedly a prerequisite for many basic tasks, unlike specific factual knowledge. It is often measured with multiple-choice questions (MCQ) benchmarks, e.g. HellaSwag and PIQA. Some of these benchmarks, however, are outdated and contain numerous validity issues. We illustrate some typical validity issues with a case study on HellaSwag, one of the most popular and problematic benchmarks for commonsense reasoning. The issues we find range from basic ungrammaticality and numerous typos to misleading prompts or equally correct options. We show that if we remove question prompts or replace them with "Lorem ipsum dolor...", about 68% of model predictions remain the same. We argue that this occurs due to inner flaws in the benchmark, not mere contamination that might be present in some models. Since benchmark scores are an essential part of model selection in both research and commercial applications, these validity issues can have severe consequences. Based on our findings, we propose BenCheck, a package for benchmark validity analysis that encapsulates the main checks performed in our case study and can be used to audit MCQ benchmarks. We apply these checks to other commonsense reasoning benchmarks.

Real-world personally identifiable information (PII) redaction often operates on document images—scans, screenshots, and PDF renderings—where OCR errors, layout structure, and visual noise determine whether sensitive information is actually removed. Existing PII benchmarks are mostly text-centric and do not measure document-level redaction risk: a page remains unsafe if even one identifier is missed. We introduce LeakageBench, a challenge set of 500 document images with 11,954 GDPR-aligned PII annotations spanning direct identifiers, linkage keys, and contextual re-identification surfaces. We evaluate generic OCR pipelines, commercial and task-adapted OCR-dependent detectors, and OCR-free vision-language models using entity-level F1, group-wise leakage, and document-level leakage metrics. Code Interpreter raises GPT-5.5 localization F1 from 0.090 to 0.249, but critical page-level leakage remains 0.968. These results show that stronger detection and tool assistance improve localization without making most pages safe for release. LeakageBench provides a diagnostic benchmark for high-recall, spatially grounded PII redaction in document images.

RAG systems are usually compared as complete pipelines, making it difficult to attribute improvements to individual components. We evaluate chunking, retrieval, fusion, and reranking through a full factorial design with paired component substitutions and equal context budgets. Across five benchmarks, chunking interacts with retrieval architecture, BM25 fusion changes direction by domain, and reranking is the only intervention we test that remains beneficial across nearly all upstream configurations. Claim-level transitions further show that similar net recall can conceal very different changes in visible evidence. These findings demonstrate that RAG components cannot be ranked independently and motivate interaction-aware evaluation instead of winner-take-all pipeline comparisons.