RenoBench: A Citation Parsing Benchmark

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.

Large language models can answer questions correctly and still hallucinate their citations. Most citation systems address this by retrieving documents at inference time. Retrieval adds latency and depends on external infrastructure. Retrieval noise can also affect the answer. We study whether LLMs can cite documents from their (continual) pretraining without test-time retrieval. We introduce CitePretrainBench to evaluate this setting. It includes Wikipedia, Common Crawl, and arXiv as real-world corpora and also includes novel, unseen documents. The benchmark includes short-form citation tasks for a single fact and long-form tasks for multiple facts. Continual-pretraining first binds factual knowledge to a persistent identifier for each document. Instruction tuning then teaches the model to put these identifiers in its answers as citations. Passive Indexing appends an identifier at the end of each document. Synthetic data used in Active Indexing restate facts in diverse, compositional forms. We train two mappings together. These mappings are source$\to$fact and fact$\to$source. The first direction involves the model generating content given a cited source. In the second, it learns to attribute its own answers when facts are paraphrased or combined. On Qwen-2.5-7B and 3B, Active Indexing improves citation precision over Passive Indexing on every evaluated citation task, with gains of up to 30.2 percentage points. In the scaling ablation, citation performance continues to improve as we add augmented data, including at 16 times the original token count. Combining internal and external citations also improves robustness to retrieval noise.

Resolving correct citations can be a time-consuming task for research writers. We address local citation recommendation (LCR), where the goal is to retrieve appropriate references for a given sentence. Web tools generally depend on the existing citation graph, which may bias results toward citation count or more recent publications. LCR research using full paper texts has been limited by the lack of publicly available data. We present a new dataset, the Local Citation Recommendation Corpus (LCRC), containing 14,735 scientific statements derived from astrophysics review papers, each with one or more citation targets whose full text is accessible via API. Using this dataset we systematically compare retrieval methods and develop a citation recommendation system that avoids citation graph bias while achieving 8.0% improvement in Recall@25 and 6.9% improvement in NDCG@25 over dense retrieval baselines without model fine-tuning. To support reproducibility, we make our code and dataset publicly available.