Large language models (LLMs) power deep research agents that synthesize information from hundreds of web sources into cited reports, yet these citations cannot be reliably verified. Current approaches either trust models to self-cite accurately, risking bias, or employ retrieval-augmented generation (RAG) that does not validate source accessibility, relevance, or factual consistency. We introduce the first source attribution evaluation framework that uses a reproducible AST parser to extract and evaluate inline citations from LLM-generated Markdown reports at scale. Unlike methods that verify claims in isolation, our framework closes the loop by retrieving the actual cited content, enabling human or model evaluators to judge each citation against its source. Citations are evaluated along three dimensions. (1) Link Works verifies URL accessibility, (2) Relevant Content measures topical alignment, and (3) Fact Check validates factual accuracy against source content. We benchmark 14 closed-source and open-source LLMs across three evaluation dimensions using rubric-based LLM-as-a-judge evaluators calibrated through human review. Our results reveal that even the strongest frontier models maintain link validity above 94% and relevance above 80%, yet achieve only 39-77% factual accuracy, while fewer than half of open-source models successfully generate cited reports in a one-shot setting. Ablation studies on research depth show that Fact Check accuracy drops by approximately 42% on average across two frontier models as tool calls scale from 2 to 150, demonstrating that more retrieval does not produce more accurate citations. These findings reveal a critical disconnect between surface-level citation quality and factual reliability, and our framework provides the evaluation infrastructure to assess the disconnect.

翻译：大语言模型驱动的研究代理能够综合数百个网络来源的信息生成带引用的报告，但这些引用无法得到可靠验证。当前方法要么依赖模型自行准确引用（存在偏差风险），要么采用检索增强生成技术，但后者无法验证来源的可访问性、相关性或事实一致性。我们首次提出来源归属评估框架，通过可复现的AST解析器，从大语言模型生成的Markdown报告中大规模提取并评估行内引用。与孤立验证声明的方法不同，我们的框架通过检索实际引用内容形成完整闭环，使人或模型评估者能够将每处引用与其来源进行对照。评估从三个维度展开：（1）链接有效性验证URL可访问性；（2）内容相关性衡量主题对齐程度；（3）事实核查验证引用内容与来源文本的事实准确性。我们基于人工校准的LLM-as-a-judge评估标准，对14个闭源和开源大语言模型在三个评估维度上进行基准测试。结果显示，即使最先进的模型在链接有效性（94%以上）和相关性（80%以上）方面表现优异，事实准确性却仅为39-77%；同时，在单次生成情境下，仅有不到半数的开源模型能成功生成带引用的报告。针对研究深度消融实验表明，当工具调用次数从2次增至150次时，两大前沿模型的事实准确性平均下降约42%，说明更多检索并不能带来更精准的引用。这些发现揭示了表面引用质量与事实可靠性之间的关键脱节，而我们的框架提供了评估这一脱节的基础设施。