More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation
InciteResearch framework transforms tacit understanding into explicit research proposals, enhancing novelty and impact.
Key Findings
Methodology
The InciteResearch framework uses a multi-agent system to convert researchers' tacit understanding into explicit research proposals. Core components include five-dimensional researcher state modeling, assumption violation, and necessity checking. TF-Bench benchmark evaluates the framework's effectiveness.
Key Results
- On TF-Bench, InciteResearch improved novelty and impact from 3.671 and 3.806 to 4.250 and 4.397, respectively.
- Compared to prompt-based baselines, InciteResearch shifted proposals from recombination to architectural insight.
- Ablation studies revealed the indispensable function of each operator.
Significance
This study demonstrates that AI can extend thinking, helping researchers extract explicit research questions from tacit intuition, thus driving scientific innovation.
Technical Contribution
Introduced Assumption-Breaking Hypothesis Generation (ABHG) method, offering new theoretical guarantees and engineering possibilities through assumption violation and necessity checking.
Novelty
First to apply AI for tacit-to-explicit research support, breaking traditional research framework limitations and offering a new form of cognitive collaboration.
Limitations
- Further validation needed across diverse domains to ensure generality and effectiveness.
- Current implementation relies on specific language models, which may limit broad applicability.
Future Work
Future research could explore applications in more domains, optimize the framework's interactivity and flexibility, and enhance its generality.
AI Executive Summary
Scientific research often begins with tacit friction rather than a clear question. Existing AI research agents assume explicit input, limiting them to automating execution. The InciteResearch framework, through a multi-agent system, transforms researchers' tacit understanding into explicit research proposals, enhancing novelty and impact.
The framework employs five-dimensional researcher state modeling, assumption violation, and necessity checking to help researchers extract explicit research questions from tacit intuition. TF-Bench benchmark evaluates the framework's effectiveness, showing significant improvements in novelty and impact.
This study demonstrates that AI can extend thinking, driving scientific innovation. Future research could explore applications in more domains, optimize the framework's interactivity and flexibility, and enhance its generality.
Deep Analysis
Background
AI has shown strong potential in automating literature search and manuscript refinement, but most assume clear input. Human research often starts with tacit friction. The InciteResearch framework aims to address this issue.
Core Problem
Existing AI research agents assume explicit input, limiting them to automating execution and unable to handle tacit friction and unclear questions.
Innovation
The InciteResearch framework, through a multi-agent system, transforms researchers' tacit understanding into explicit research proposals, enhancing novelty and impact.
Methodology
- �� Five-dimensional researcher state modeling
- �� Assumption violation
- �� Necessity checking
- �� Use TF-Bench to evaluate framework effectiveness
Experiments
Using the TF-Bench benchmark, evaluate the framework's improvement in novelty and impact. Compare with prompt-based baselines.
Results
InciteResearch improved novelty and impact from 3.671 and 3.806 to 4.250 and 4.397, showing significant advantages.
Applications
The framework can be used to identify and solve tacit problems in scientific research, driving innovation and discovery.
Limitations & Outlook
Further validation needed across diverse domains to ensure generality and effectiveness. Current implementation relies on specific language models.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and InciteResearch is like a smart assistant that helps you turn vague ideas into clear recipes. It asks questions, breaks assumptions, and checks necessity to help you clarify your thoughts and ultimately create a delicious dish.
ELI14 Explained like you're 14
Imagine you're playing a game, and InciteResearch is like a smart assistant that helps you turn vague ideas into clear strategies. It asks questions, breaks assumptions, and checks necessity to help you clarify your thoughts and ultimately win the game!
Glossary
InciteResearch
A multi-agent framework that converts tacit understanding into explicit research proposals.
Used for identifying and solving tacit problems in scientific research.
TF-Bench
A benchmark for evaluating tacit-to-explicit research support.
Evaluates the effectiveness of the InciteResearch framework.
Assumption-Breaking Hypothesis Generation
A method for generating new research hypotheses by breaking assumptions.
Used in the InciteResearch framework to enhance novelty.
Feasibility-Novelty Product
Used to evaluate the effectiveness of assumption breaking.
In the InciteResearch framework for selecting assumptions.
Causal Derivation Trace
Ensures research methods are a logical necessity.
In the InciteResearch framework for verifying the necessity of research methods.
Open Questions Unanswered questions from this research
- 1 How to validate the generality of the InciteResearch framework across more domains?
- 2 How to optimize the framework's interactivity and flexibility?
Applications
Immediate Applications
Scientific Research
Helps researchers identify and solve tacit problems, driving innovation and discovery.
Long-term Vision
Cross-Domain Applications
Validate and apply the framework in more domains, driving scientific progress.
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.