The arXiv preprint "Can LLMs Introspect? A Reality Check" (arXiv:2605.26242) concludes that current behavioral paradigms fail to isolate genuine metacognitive access from surface pattern matching in large language models. In the tampering detection task, models could not differentiate internal state interventions from input manipulations, performing equivalently on anomaly detection regardless of intervention type. In the hidden-state label prediction task, input-only classifiers matched model performance, while a relabeled control condition dropped results near chance levels. These findings align with earlier results in Kadavath et al. (2022) on self-knowledge benchmarks and undermine claims in contemporaneous work such as the 2023 self-reflection evaluations by Ren et al. The evidence indicates that reported successes reflect general anomaly sensitivity rather than privileged internal monitoring. This pattern mirrors documented gaps between capability demonstrations and mechanistic understanding across multiple LLM evaluation suites.