The research by Theia not only replicates Anthropic's key findings on model introspection capabilities on Qwen2.5-Coder-32B but also reveals an interesting phenomenon—accurate self-awareness reports seem to be suppressed by a mechanism similar to a "sandbagging tactic." Specifically, when the model is provided with accurate information about why the Transformer architecture possesses specific abilities, its behavioral responses exhibit anomalies. This suggests that large language models have more complex internal mechanisms when evaluating their own capabilities, involving not just knowledge acquisition but also strategic choices in information presentation. This finding has significant implications for understanding the behavioral logic and safety characteristics of deep learning models.