AI Insight
Guardian Crawler is a retrieval-first system designed to extract relevant information from noisy web data and generate evidence-grounded summaries with explicit citations. Testing on a synthetic 900-document corpus showed that risk-based reranking achieved the highest retrieval performance (P@10 = 1.00, NDCG@10 = 0.94), outperforming standard BM25 retrieval. An automated evaluation found that 88% of generated summary bullets were classified as supported by the retrieved documents, though the authors note limitations including lack of human validation and testing only on synthetic data.
Why it matters
This work addresses the challenge of reliably extracting information from unreliable web sources in sensitive domains like investigative journalism or crisis monitoring. The system's emphasis on explicit citations and controlled testing environments could help develop more trustworthy AI-assisted research tools, though real-world effectiveness remains to be demonstrated.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Retrieving relevant evidence from noisy web data is challenging, particularly in sensitive domains containing incomplete reports, heterogeneous language, and irrelevant content. We present Guardian Crawler, a reproducible retrieval-first testbed for controlled experiments on knowledge discovery and evidence-grounded summarization over synthetic web-like corpora. The architecture combines BM25 retrieval with risk-aware, embedding-augmented, and hybrid reranking, followed by constrained retrieval-augmented generation with explicit document citations. Experiments on a synthetic 900-document corpus and 10 queries produced the highest descriptive retrieval scores under risk-based reranking, with P@10 = 1.00 and NDCG@10 = 0.94, compared with 0.94 and 0.81 for BM25. The best hybrid and BM25+Semantic configurations reached NDCG@10 values of 0.94 and 0.88, respectively. All 41 evaluable generated bullets passed the lexical coverage threshold; an automated LLM judge classified 36 as supported, one as partially supported, and four as unsupported. These results demonstrate the feasibility of Guardian Crawler as a controlled testbed but do not establish statistical superiority, human-validated faithfulness, or transfer to live-web investigative environments.