ResearchQA: Benchmarking Citation-Grounded Question-Answering on Scientific Papers

• URL: http://arxiv.org/abs/2607.11074v1
• Date: Mon, 13 Jul 2026 04:26:51 GMT
• Title: ResearchQA: Benchmarking Citation-Grounded Question-Answering on Scientific Papers
• Abstract summary: ResearchQA is a benchmark of 6,211 single-paper question-answer pairs from 494 open-access papers.<n>ResearchQA is designed for citation-grounded evaluation.<n>We evaluate eight leading closed- and open-weight models in a citation-grounded chat-with-paper setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models are increasingly used to assist scientific reading, but existing evaluation methods often fail to detect whether answers are supported by verifiable citations. We introduce ResearchQA, a benchmark of 6,211 single-paper question-answer pairs from 494 open-access papers spanning eight domains and four question types: lookup, comprehension, multi-hop, and adversarial. ResearchQA is designed for citation-grounded evaluation: it permits multiple valid supporting passages for a claim and rewards grounded refusal when the source paper does not support an answer. We evaluate eight leading closed- and open-weight models in a citation-grounded chat-with-paper setting using a deterministic citation matcher and an LLM-based rubric evaluator. Citation-based metrics separate systems more clearly than LLM-evaluator scores: section coverage and citation accuracy vary substantially across models, while evaluator scores remain tightly compressed. We further find that open-weight models approach the best closed-model citation accuracy while achieving 3 to 6 times lower per-example latency. We release the benchmark, evaluation harness, and evaluator prompt.

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