Reproduce “Orchestrating Specialized Agents for Trustworthy Enterprise RAG” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Xincheng You, Qi Sun, Neha Bora, Huayi Li, Shubham Goel, Kang Li, Sean Culatana
Atlassian researchers developed ADORE (Adaptive Deep Orchestration for Research in Enterprise), an agentic framework that enhances Retrieval-Augmented Generation (RAG) for complex enterprise knowledge tasks by providing trustworthy, traceable, and comprehensive reports through an iterative, evidence-driven workflow. This framework achieved a #1 rank on the DeepResearch Bench leaderboard and a 77.21% win rate against competitors in side-by-side evaluations.
Key insights
- Memory-locked Synthesis enables the generation of reports where every claim is explicitly linked back to its supporting evidence in a structured Memory Bank, enhancing trust and auditability by design.
- Evidence-coverage–guided Execution provides an adaptive, iterative mechanism for systematic evidence completeness checks, triggering targeted follow-up retrieval and ensuring the process converges when all required evidence is gathered.
- Section-packed Long-context Grounding efficiently overcomes LLM context window limitations for long-form reports by dynamically packing, pruning, and compressing section-relevant evidence while maintaining citation integrity.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.