More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation

2026-05-07

AI Summary

This study addresses the challenge that existing AI research agents struggle to navigate the ambiguous and implicit cognitive friction characteristic of early-stage scientific inquiry. To bridge this gap, the authors propose InciteResearch, a multi-agent framework that employs Socratic questioning chains to transform researchers’ tacit understanding into explicit, testable questions. The work introduces TF-Bench, the first pre-problem research assistance benchmark, and integrates five-dimensional researcher state modeling, a seven-stage causal reasoning pipeline, and a necessity-validation mechanism within a multi-agent collaborative architecture. By combining structured cognitive modeling with joint optimization of feasibility and novelty, InciteResearch significantly outperforms prompt-based baselines on TF-Bench, elevating novelty and impact scores from 3.671/3.806 to 4.250/4.397, respectively, and shifting generated proposals from mere recombination toward architectural-level insights.

Abstract

AI research agents have shown strong potential in automating literature search and manuscript refinement, yet most assume a clear and actionable initial input, operating only after a research question has been made explicit. In contrast, human research often begins with tacit friction, a sense of misalignment before a question can be formed. We introduce InciteResearch, a multi-agent framework designed to make a researcher's implicit understanding explicit, inspectable, and actionable. InciteResearch decomposes the logical chain of Socratic questioning and distributes it across the entire pipeline that: (1) Elicits a structured five-dimensional researcher profile state anchored by specific friction points from vague, even domain-unrelated inputs; (2) Violates hidden assumptions by maximizing the feasibility-novelty product with enforcing a 7-stage causal derivation trace; and (3) check whether the proposed method is a Necessary consequence of the reframed insight. We further introduce TF-Bench, the first benchmark for tacit-to-explicit research assistance that distinguishes domain-related from domain-unrelated inspirations across four scientific modes. On TF-Bench, InciteResearch achieves leapfrogging gains over a prompt-based baseline (novelty/impact from 3.671/3.806 to 4.250/4.397), shifting generated proposals from recombination to architectural insight. Our work demonstrates that AI can serve as an extension of thinking itself, rather than merely automating downstream execution.

Problem

• scientific ideation
• tacit friction
• pre-question research
• research question formulation
• implicit understanding

Innovation

• multi-agent framework
• tacit-to-explicit ideation
• Socratic questioning
• causal derivation trace
• research friction