Papers with citation-based long-context understanding

1 papers
SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning (2026.eacl-long)

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Challenge: Long-context understanding is a critical capability for large language models . evaluating this capability requires extensive human annotation, which is time-consuming and costly.
Approach: They propose a benchmark to assess citation-grounded long-context reasoning in academic writing.
Outcome: The proposed benchmark compares state-of-the-art models with human experts on two tasks . human experts achieve 90% accuracy, but most models struggle with the cloze-style task .

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