During grounded generation, language models evaluate candidate pages within a strict context window and discard retrieved sources that lack concise, query-relevant factual density. When competitor pages offer more direct answers, cleaner extractive passages, or stronger domain authority signals, the model selects those higher-scoring snippets to support its response while bypassing less focused alternatives.