Agentic systems have recently emerged as a promising tool to automate literature-based ideation. However, current systems often remain black-box, with limited transparency or control for researchers. Our work introduces TrustResearcher, a multi-agent demo system for knowledge-grounded and transparent ideation. Specifically, TrustResearcher integrates meticulously designed four stages into a unified framework: (A) Structured Knowledge Curation, (B) Diversified Idea Generation, (C) Multi-stage Idea Selection, and (D) Expert Panel Review and Synthesis. Different from prior pipelines, our system not only exposes intermediate reasoning states, execution logs, and configurable agents for inspections, but also enables diverse and evidence-aligned idea generation. Our design is also domain-agnostic, where the same pipeline can be instantiated in any scientific field. As an illustrative case, we demonstrate TrustResearcher on a graph-mining scenario (k-truss breaking problem), where it generates distinct, plausible candidates with evidence and critiques. A live demo and source code are available at https://github.com/valleysprings/TrustResearcher

翻译：智能体系统近来已成为自动化文献驱动研究构思的一种有前景的工具。然而，当前系统往往仍是黑箱，对研究者而言透明度和可控性有限。本研究提出了TrustResearcher，一个用于知识驱动与透明化构思的多智能体演示系统。具体而言，TrustResearcher将精心设计的四个阶段整合到一个统一框架中：(A) 结构化知识整理，(B) 多样化构思生成，(C) 多阶段构思筛选，以及(D) 专家小组评审与综合。与先前流程不同，我们的系统不仅公开中间推理状态、执行日志和可配置的智能体以供检查，还能实现多样化且与证据对齐的构思生成。我们的设计也与领域无关，同一流程可在任何科学领域实例化。作为一个示例案例，我们在图挖掘场景（k-truss破坏问题）中演示了TrustResearcher，该系统能够生成具有证据和批判性分析的不同且合理的候选方案。实时演示与源代码可在 https://github.com/valleysprings/TrustResearcher 获取。