Search agents mitigate hallucination by grounding their answers in retrieved web results. However, retrieval-based approaches also introduce an attack surface: agents may cite misinformation from poisoned search corpora containing false or malicious documents. We demonstrate that, in some cases, search agents’ reasoning and responses may be steered by completely factual but distracting information. We refer to this failure as lazy grounding. We expose lazy grounding by injecting nearby evidence from answer-changing rewrites of benchmark questions into the search corpora. Each document contains factual evidence that supports a neighboring rewritten question but is retrieved for the original question. Across 12 model–benchmark pairs, the attack causes the accuracy of search agents’ responses to drop by 5.9 points on average and by up to 17.3 points, while inducing nearby-answer adoption in every setting. The effect is even stronger when nearby evidence appears later or is more answer-shaped. Our results show that robust search agents must defend against not only misinformation but also the misapplication of factual evidence. The code is publicly available at https://github.com/frankyzha/lazy-grounding.

We tested over 20 Transformer models for ranking long documents (including recent LongP models trained with FlashAttention and RankGPT models “powered” by OpenAI and Anthropic cloud APIs).We compared them with the simple FirstP baseline, which applied the same model to truncated input (up to 512 tokens).On MS MARCO, TREC DL, and Robust04 no long-document model outperformed FirstP by more than 5% (on average).We hypothesized that this lack of improvement is not due to inherent model limitations,but due to benchmark positional bias (most relevant passages tend to occur early in documents),which is known to exist in MS MARCO.To confirm this, we analyzed positional relevance distributions across four long-document corpora (with six query sets) and observed the same early-position bias.Surprisingly, we also found bias in six BEIR collections, which are typically categorized asshort-document datasets.We then introduced a new diagnostic dataset, MS MARCO FarRelevant, where relevant spans were deliberately placed beyond the first 512 tokens.On this dataset, many long-context models—including RankGPT—performed at random-baseline level, suggesting overfitting to positional bias.We also experimented with debiasing training data, but with limited success.Our findings (1) highlight the need for careful benchmark design in evaluating long-context models for document ranking, (2) identify model types that are more robust to positional bias, and (3) motivate further work on approaches to debias training data.We release our code and data to support further research.