Retrieval-Augmented Generation (RAG) shows promise for enterprise knowledge work, yet it often underperforms in high-stakes decision settings that require deep synthesis, strict traceability, and recovery from underspecified prompts. One-pass retrieval-and-write pipelines frequently yield shallow summaries, inconsistent grounding, and weak mechanisms for completeness verification. We introduce ADORE (Adaptive Deep Orchestration for Research in Enterprise), an agentic framework that replaces linear retrieval with iterative, user-steered investigation coordinated by a central orchestrator and a set of specialized agents. ADORE's key insight is that a structured Memory Bank (a curated evidence store with explicit claim-evidence linkage and section-level admissible evidence) enables traceable report generation and systematic checks for evidence completeness. Our contributions are threefold: (1) Memory-locked synthesis - report generation is constrained to a structured Memory Bank (Claim-Evidence Graph) with section-level admissible evidence, enabling traceable claims and grounded citations; (2) Evidence-coverage-guided execution - a retrieval-reflection loop audits section-level evidence coverage to trigger targeted follow-up retrieval and terminates via an evidence-driven stopping criterion; (3) Section-packed long-context grounding - section-level packing, pruning, and citation-preserving compression make long-form synthesis feasible under context limits. Across our evaluation suite, ADORE ranks first on DeepResearch Bench (52.65) and achieves the highest head-to-head preference win rate on DeepConsult (77.2%) against commercial systems.

翻译：检索增强生成（RAG）技术在企业知识工作中展现出潜力，但在需要深度综合、严格可追溯性以及对模糊提示进行恢复的高风险决策场景中，其表现往往不尽如人意。单次检索-生成流水线常产生浅层摘要、不一致的论证依据以及薄弱的完整性验证机制。本文提出ADORE（企业研究自适应深度协同框架），一种智能体驱动的框架，通过中央协调器与一组专业化智能体的协同工作，将线性检索替换为由用户引导的迭代式调查过程。ADORE的核心思想在于：结构化记忆库（一种具备显式主张-证据关联及章节级可采纳证据的精选知识存储）能够实现可追溯的报告生成与系统化的证据完整性检验。我们的贡献主要包括三个方面：（1）记忆锁定的合成机制——报告生成受限于具备章节级可采纳证据的结构化记忆库（主张-证据图），从而实现可追溯的主张与有据可查的引用；（2）证据覆盖度引导的执行流程——通过检索-反思循环审计章节级证据覆盖度，触发定向的后续检索，并依据证据驱动的停止准则终止流程；（3）章节封装的长上下文关联——通过章节级封装、剪枝及保留引用的压缩技术，使长文本合成在上下文限制下得以实现。在评估体系中，ADORE在DeepResearch Bench上以52.65分位列第一，并在DeepConsult上以77.2%的胜率获得对商业系统的最高直接偏好胜率。