Researchers build librarian-writer AI system for trustworthy reports
A two-tier AI system eliminates hallucinations with zero contradictions in 6,845 tests.
Researchers from four universities (Xing Zhang, Yanwei Cui, Guanghui Wang, Peiyang He) have developed a novel two-tier agentic system for generating reliable long-form research reports. The system, detailed in their arXiv paper (arXiv:2608.12984), separates knowledge management from report composition using a deterministic 'librarian' agent and a portable multi-agent 'writer' runtime.
The librarian ingests timestamped sources (SEC filings, government statistics, Wikipedia) into a trust-tiered ontology that includes evidence cards, a metric ledger, and a claim graph. The writer then composes reports using only evidence prior to a specified knowledge cutoff, eliminating look-ahead bias. In testing on 6,130 sources (555,926 evidence cards), the system eliminated 6,845 cross-section contradictions while maintaining zero contradictions across seven evaluation cutoffs. Red-team feedback automatically propagates corrections to the knowledge base without manual edits.
- Eliminates 6,845 contradictions across 555,926 evidence cards from SEC filings and government data
- Automatically propagates corrections from red-team feedback with zero manual edits
- Runs 3.7x faster than serial processing while maintaining quality exceeding all-Opus baselines
Why It Matters
Professionals can finally trust AI-generated reports to maintain factual consistency over time without manual verification.