Large language models drop in performance when their context contains semantic distractors: context topically related to the question but off the reasoning chain. Prior mechanistic work tracks a single injected distractor token, missing failures in which the model predicts other context tokens or out-of-context but topically related ones. We instead track the model’s actual top non-gold prediction, providing a localization target for every observed top-1 failure in the analyzed subset. Failures converge on a small set of mid-to-late-layer answer-selection heads (AS-Heads): patching their clean outputs into distracting runs recovers 60-86\\%$ of failed predictions across four base models from two families (random heads recover $\leq 8\\%$), with the mechanism also replicating in two instruction-tuned models. Clean-prompt ablations indicate that these heads also contribute to normal answer selection, so their role is not specific to distractors. Redirecting their attention onto an entity span causes the model to transcribe that span at $22$-$83\\%$ (vs. $\leq 7.4\\%$ for random heads), establishing a causally consequential intervention point. The localized heads transfer across MuSiQue, HotpotQA, 2WikiMultiHopQA, and non-Wikipedia RAG settings. At inference, their attention provides a training-free context-filtering signal that lifts F1 by up to $+35$ points on disrupted samples and is competitive with retrieval-head methods.

It presents significant challenges to generate comprehensive and accurate Wikipedia articles for newly emerging events under real-world scenario. Existing attempts fall short either by focusing only on short snippets or by using metrics that are insufficient to evaluate real-world scenarios. In this paper, we construct WIKIGENBENCH, a new benchmark consisting of 1,320 entries, designed to align with real-world scenarios in both generation and evaluation. For generation, we explore a real-world scenario where structured, full-length Wikipedia articles with citations are generated for new events using input documents from web sources. For evaluation, we integrate systematic metrics and LLM-based metrics to assess the verifiability, organization, and other aspects aligned with real-world scenarios. Based on this benchmark, we conduct extensive experiments using various models within three commonly used frameworks: direct RAG, hierarchical structure-based RAG, and RAG with fine-tuned generation model. Experimental results show that hierarchical-based methods can generate more comprehensive content, while fine-tuned methods achieve better verifiability. However, even the best methods still show a significant gap compared to existing Wikipedia content, indicating that further research is necessary.