More Than Can Be Said: A Benchmark and Framework fo... | AI Research

Key Takeaways

More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation Most AI research tools are designed to assist with tasks that occur aft...
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.
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.

Paper AbstractExpand

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.

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

Most AI research tools are designed to assist with tasks that occur after a research question has already been clearly defined, such as literature reviews or manuscript editing. However, the early stages of scientific inquiry are often characterized by "tacit friction"—a vague sense of misalignment or intellectual discomfort that exists before a formal question can even be articulated. This paper introduces a new framework called InciteResearch, which aims to help researchers transform these initial, implicit hunches into concrete, actionable research paths.

Bridging the Gap from Intuition to Inquiry

InciteResearch is a multi-agent framework designed to act as an extension of the researcher’s own thinking process. Instead of requiring a polished prompt, the system accepts vague or even domain-unrelated inputs to begin its work. It functions by breaking down the complex process of Socratic questioning into a structured pipeline. This pipeline helps researchers move from a state of uncertainty to a clear, inspectable research direction by mapping out their implicit understanding into a five-dimensional profile anchored by specific points of friction.

The Mechanics of Ideation

To ensure that the resulting research proposals are both creative and grounded, the framework employs a rigorous three-step process:

Profiling: It elicits a structured researcher profile based on the input’s specific friction points. 2. Assumption Testing: It challenges hidden assumptions by enforcing a 7-stage causal derivation trace, specifically designed to maximize the balance between the feasibility and the novelty of an idea. 3. Logical Validation: It verifies that the final proposed method is a necessary consequence of the reframed insight, ensuring logical consistency.

Measuring Success with TF-Bench

The authors also introduce TF-Bench, the first benchmark specifically designed to evaluate how well AI can assist in the "tacit-to-explicit" phase of research. This benchmark tests the ability of an agent to draw inspiration from both domain-related and domain-unrelated sources across four different scientific modes.
In testing, InciteResearch demonstrated significant improvements over standard prompt-based baselines. The framework successfully shifted the nature of generated proposals from simple "recombination"—merely mixing existing ideas—to "architectural insight," which represents a deeper level of scientific contribution. Specifically, the system saw notable gains in both novelty and impact scores, suggesting that AI can play a more fundamental role in the creative stages of scientific discovery.