SealQA explained: testing search agents on conflicting and noisy results

SealQA is a Virginia Tech benchmark, published at ICLR 2026, designed to test search-augmented language models on factual questions for which web results are conflicting, outdated, misleading, or fail to contain the answer. Its 111-question Seal-0 subset consists of questions that several frontier models consistently failed, while Seal-Hard expands to 254 difficult questions and LongSeal tests models’ ability to locate one relevant document among numerous plausible distractors. Questions emphasize advanced reasoning, entity disambiguation, temporal tracking, occasional cross-lingual retrieval, and false-premise detection, with labels for freshness and expected search behavior. The paper reports that search and additional reasoning do not consistently improve performance and can worsen it when models overweight noisy evidence, while recent or fast-changing facts and long contexts full of hard negatives are especially difficult. The accompanying discussion argues that effective search tools should expose source dates and provenance, support recency and domain filtering, prioritize a small number of high-quality results over large context dumps, and allow abstention when evidence remains uncertain. It also notes limitations including small sample sizes, changing answers, public-data contamination risks, and the benchmark’s adversarial selection against models available in 2025.

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