TrustResearcher: Automating Knowledge-Grounded and Transparent Research Ideation with Multi-Agent Collaboration

TL;DR

TrustResearcher is a transparent, multi-agent system that automates scientific ideation with explainable reasoning, enabling domain-agnostic, evidence-based research idea generation and review.

Contribution

It introduces a novel multi-stage, transparent framework for automated research ideation that exposes reasoning and logs, unlike previous black-box systems.

Findings

Generates diverse, evidence-aligned research ideas

Provides transparent intermediate reasoning states

Demonstrates effectiveness in a graph-mining case study

Abstract

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